{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/PoP_replication.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}29 Aug 2024, 21:00:54
{txt}
{com}. 
. version 18.0
{txt}
{com}. set more off
{txt}
{com}. clear mata
{txt}
{com}. 
. *1) Set working directories
. *global DATADIR  "SET YOUR DATA DIRECTORY" 
. *global GRAPHDIR "SET YOUR GRAPH DIRECTORY"
. *global RESULTSDIR "SET YOUR GRAPH DIRECTORY"
. 
. *1) Load data
. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. *2) Descriptive analyses:
. 
. *2a) Results reported in the discussion
. tab paper_id

{txt}group(paper {c |}
       _id) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         12        3.05        3.05
{txt}          2 {c |}{res}         10        2.54        5.60
{txt}          3 {c |}{res}          2        0.51        6.11
{txt}          4 {c |}{res}          6        1.53        7.63
{txt}          5 {c |}{res}          3        0.76        8.40
{txt}          6 {c |}{res}         18        4.58       12.98
{txt}          7 {c |}{res}         37        9.41       22.39
{txt}          8 {c |}{res}          1        0.25       22.65
{txt}          9 {c |}{res}          4        1.02       23.66
{txt}         10 {c |}{res}          5        1.27       24.94
{txt}         11 {c |}{res}          4        1.02       25.95
{txt}         12 {c |}{res}          7        1.78       27.74
{txt}         14 {c |}{res}         12        3.05       30.79
{txt}         15 {c |}{res}         12        3.05       33.84
{txt}         16 {c |}{res}          2        0.51       34.35
{txt}         17 {c |}{res}         16        4.07       38.42
{txt}         18 {c |}{res}          8        2.04       40.46
{txt}         19 {c |}{res}          1        0.25       40.71
{txt}         20 {c |}{res}         16        4.07       44.78
{txt}         21 {c |}{res}         12        3.05       47.84
{txt}         22 {c |}{res}         24        6.11       53.94
{txt}         23 {c |}{res}          3        0.76       54.71
{txt}         24 {c |}{res}          3        0.76       55.47
{txt}         25 {c |}{res}         13        3.31       58.78
{txt}         26 {c |}{res}          6        1.53       60.31
{txt}         27 {c |}{res}          8        2.04       62.34
{txt}         28 {c |}{res}         10        2.54       64.89
{txt}         29 {c |}{res}         30        7.63       72.52
{txt}         30 {c |}{res}         14        3.56       76.08
{txt}         31 {c |}{res}         15        3.82       79.90
{txt}         32 {c |}{res}          3        0.76       80.66
{txt}         33 {c |}{res}         27        6.87       87.53
{txt}         34 {c |}{res}          1        0.25       87.79
{txt}         35 {c |}{res}          5        1.27       89.06
{txt}         36 {c |}{res}          7        1.78       90.84
{txt}         37 {c |}{res}          2        0.51       91.35
{txt}         40 {c |}{res}          4        1.02       92.37
{txt}         42 {c |}{res}          2        0.51       92.88
{txt}         44 {c |}{res}          3        0.76       93.64
{txt}         45 {c |}{res}          2        0.51       94.15
{txt}         46 {c |}{res}          9        2.29       96.44
{txt}         47 {c |}{res}          8        2.04       98.47
{txt}         48 {c |}{res}          6        1.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        393      100.00
{txt}
{com}. 
. egen paper_gr=group(paper_id) 
{txt}
{com}. su paper_gr //43 unique papers

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}paper_gr {c |}{res}        393    20.63613    11.43337          1         43
{txt}
{com}. 
. bysort paper_gr: gen paper_count=_N 
{txt}
{com}. su paper_count

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}paper_count {c |}{res}        393    16.33842    10.31667          1         37
{txt}
{com}. 
. 
. *Results per study: count/study
. bysort paper_gr: gen paper_nvals=_n if partyeffect!=.
{txt}
{com}. su paper_gr

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}paper_gr {c |}{res}        393    20.63613    11.43337          1         43
{txt}
{com}. su paper_nvals

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}paper_nvals {c |}{res}        393    8.669211    7.595816          1         37
{txt}
{com}. egen modelid=group(id_models)
{txt}
{com}. su modelid

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}modelid {c |}{res}        393    163.6361    95.41666          1        330
{txt}
{com}. 
. 
. *Year
. tab year if paper_nvals==1

{txt}Publication {c |}
       year {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       1999 {c |}{res}          1        2.33        2.33
{txt}       2002 {c |}{res}          2        4.65        6.98
{txt}       2003 {c |}{res}          2        4.65       11.63
{txt}       2004 {c |}{res}          1        2.33       13.95
{txt}       2005 {c |}{res}          1        2.33       16.28
{txt}       2006 {c |}{res}          2        4.65       20.93
{txt}       2008 {c |}{res}          3        6.98       27.91
{txt}       2009 {c |}{res}          4        9.30       37.21
{txt}       2010 {c |}{res}          2        4.65       41.86
{txt}       2011 {c |}{res}          3        6.98       48.84
{txt}       2012 {c |}{res}          1        2.33       51.16
{txt}       2013 {c |}{res}          1        2.33       53.49
{txt}       2014 {c |}{res}          2        4.65       58.14
{txt}       2015 {c |}{res}          2        4.65       62.79
{txt}       2016 {c |}{res}          1        2.33       65.12
{txt}       2017 {c |}{res}          4        9.30       74.42
{txt}       2018 {c |}{res}          2        4.65       79.07
{txt}       2019 {c |}{res}          3        6.98       86.05
{txt}       2020 {c |}{res}          2        4.65       90.70
{txt}       2021 {c |}{res}          1        2.33       93.02
{txt}       2022 {c |}{res}          1        2.33       95.35
{txt}       2023 {c |}{res}          2        4.65      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         43      100.00
{txt}
{com}. su year if paper_nvals==1 & partyeffect!=.,d

                      {txt}Publication year
{hline 61}
      Percentiles      Smallest
 1%    {res}     1999           1999
{txt} 5%    {res}     2002           2002
{txt}10%    {res}     2003           2002       {txt}Obs         {res}         43
{txt}25%    {res}     2008           2003       {txt}Sum of wgt. {res}         43

{txt}50%    {res}     2012                      {txt}Mean          {res} 2012.372
                        {txt}Largest       Std. dev.     {res}  6.39577
{txt}75%    {res}     2018           2021
{txt}90%    {res}     2020           2022       {txt}Variance      {res} 40.90587
{txt}95%    {res}     2022           2023       {txt}Skewness      {res}-.1537826
{txt}99%    {res}     2023           2023       {txt}Kurtosis      {res} 2.035691
{txt}
{com}. 
. *Journals
. tab journal if paper_nvals==1, sort

                                {txt}Journal {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                 Socio-Economic Review  {c |}{res}          4        9.30        9.30
{txt}                         World Politics {c |}{res}          4        9.30       18.60
{txt}      American Political Science Review {c |}{res}          3        6.98       25.58
{txt}          Comparative Political Studies {c |}{res}          2        4.65       30.23
{txt}International Journal of Comparative .. {c |}{res}          2        4.65       34.88
{txt}                          Social Forces {c |}{res}          2        4.65       39.53
{txt}               Social Science Quarterly {c |}{res}          2        4.65       44.19
{txt}  American Journal of Political Science {c |}{res}          1        2.33       46.51
{txt}           American Sociological Review {c |}{res}          1        2.33       48.84
{txt}   British Journal of Political Science {c |}{res}          1        2.33       51.16
{txt}                   Comparative Politics {c |}{res}          1        2.33       53.49
{txt}                        Economic Record {c |}{res}          1        2.33       55.81
{txt}             Economic Sociology of Work {c |}{res}          1        2.33       58.14
{txt}  European Journal of Political Economy {c |}{res}          1        2.33       60.47
{txt} European Journal of Political Research {c |}{res}          1        2.33       62.79
{txt}      European Political Science Review {c |}{res}          1        2.33       65.12
{txt}           European Sociological Review {c |}{res}          1        2.33       67.44
{txt}                European Union Politics {c |}{res}          1        2.33       69.77
{txt}            Journal of Economic Studies {c |}{res}          1        2.33       72.09
{txt}      Journal of European Social Policy {c |}{res}          1        2.33       74.42
{txt}                  Oxford Economic Paper {c |}{res}          1        2.33       76.74
{txt}                     Policy and Society {c |}{res}          1        2.33       79.07
{txt}          Political Research Quarterly  {c |}{res}          1        2.33       81.40
{txt}                     Politics & Society {c |}{res}          1        2.33       83.72
{txt}                                 Polity {c |}{res}          1        2.33       86.05
{txt}Research in Social Stratification and.. {c |}{res}          1        2.33       88.37
{txt}Review of International Political Eco.. {c |}{res}          1        2.33       90.70
{txt}                  Socio-Economic Review {c |}{res}          1        2.33       93.02
{txt}                     Sociological Forum {c |}{res}          1        2.33       95.35
{txt}               Sociology of Development {c |}{res}          1        2.33       97.67
{txt}                      The World Economy {c |}{res}          1        2.33      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}         43      100.00
{txt}
{com}. 
. *Partisan effect
. su partyeffect //37-63

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        393    .3740458    .4844923          0          1
{txt}
{com}. tab partyeffect party_control, col //no-yes: DV: 53-47 vs. Control: 70-30
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}   Party effects as
     Party {c |}        control
    effect {c |}        no        yes {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
        no {c |}{res}        91        155 {txt}{c |}{res}       246 
           {txt}{c |}{res}     52.60      70.45 {txt}{c |}{res}     62.60 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
       yes {c |}{res}        82         65 {txt}{c |}{res}       147 
           {txt}{c |}{res}     47.40      29.55 {txt}{c |}{res}     37.40 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       173        220 {txt}{c |}{res}       393 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 
{txt}
{com}. 
. *Time effects
. su goldenshare

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}goldenshare {c |}{res}        393    .1793838    .1444232          0   .5757576
{txt}
{com}. 
. 
. **Fig. 1: Time periods and partisan effects on inequ<lity
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. xtile pct_goldenshare = goldenshare, nq(4)
{txt}
{com}. bysort pct_goldenshare: su goldenshare

{txt}{hline}
-> pct_goldenshare = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}goldenshare {c |}{res}        111           0           0          0          0

{txt}{hline}
-> pct_goldenshare = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}goldenshare {c |}{res}         98    .1374351    .0705072   .0384615   .2272727

{txt}{hline}
-> pct_goldenshare = 3

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}goldenshare {c |}{res}         94     .255201    .0218226   .2333333   .3023256

{txt}{hline}
-> pct_goldenshare = 4

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}goldenshare {c |}{res}         90    .3671143    .0466168   .3055556   .5757576

{txt}
{com}. 
. **Coefficient of variation:
. forval i=1(1)5 {c -(}
{txt}  2{com}.         summarize partyeffect if pct_goldenshare==`i'
{txt}  3{com}. di 100 * r(sd) / r(mean)        
{txt}  4{com}. {c )-}       

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        111    .2792793    .4506797          0          1
161.37241

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         98    .3163265     .467433          0          1
147.76913

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         94    .4361702    .4985681          0          1
114.30585

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         90    .4888889     .502677          0          1
102.82029

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}          0
.
{txt}
{com}. 
. ttest partyeffect if pct_goldenshare==1 | pct_goldenshare==4, by(pct_goldenshare) //sign at 0.001

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}    111{col 22} .2792793{col 34} .0427766{col 46} .4506797{col 58}  .194506{col 70} .3640525
       {txt}4 {c |}{res}{col 12}     90{col 22} .4888889{col 34} .0529868{col 46}  .502677{col 58} .3836052{col 70} .5941725
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    201{col 22} .3731343{col 34} .0341983{col 46}  .484845{col 58} .3056988{col 70} .4405699
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.2096096{col 34} .0673254{col 58}-.3423724{col 70}-.0768468
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}4{txt})                                      t = {res} -3.1134
{txt}H0: diff = 0                                     Degrees of freedom = {res}     199

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0011         {txt}Pr(|T| > |t|) = {res}0.0021          {txt}Pr(T > t) = {res}0.9989
{txt}
{com}. ttest partyeffect if pct_goldenshare==2 | pct_goldenshare==4, by(pct_goldenshare) // sign. at 0.001

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       2 {c |}{res}{col 12}     98{col 22} .3163265{col 34} .0472179{col 46}  .467433{col 58} .2226121{col 70} .4100409
       {txt}4 {c |}{res}{col 12}     90{col 22} .4888889{col 34} .0529868{col 46}  .502677{col 58} .3836052{col 70} .5941725
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    188{col 22} .3989362{col 34} .0358089{col 46} .4909871{col 58} .3282948{col 70} .4695775
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.1725624{col 34} .0707527{col 58}-.3121434{col 70}-.0329814
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}2{txt}) - mean({res}4{txt})                                      t = {res} -2.4389
{txt}H0: diff = 0                                     Degrees of freedom = {res}     186

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0078         {txt}Pr(|T| > |t|) = {res}0.0157          {txt}Pr(T > t) = {res}0.9922
{txt}
{com}. ttest partyeffect if pct_goldenshare==3 | pct_goldenshare==4, by(pct_goldenshare) // sign. at 0.1

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       3 {c |}{res}{col 12}     94{col 22} .4361702{col 34} .0514234{col 46} .4985681{col 58} .3340536{col 70} .5382868
       {txt}4 {c |}{res}{col 12}     90{col 22} .4888889{col 34} .0529868{col 46}  .502677{col 58} .3836052{col 70} .5941725
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    184{col 22} .4619565{col 34} .0368539{col 46} .4999109{col 58} .3892433{col 70} .5346697
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0527187{col 34} .0738242{col 58}  -.19838{col 70} .0929426
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}3{txt}) - mean({res}4{txt})                                      t = {res} -0.7141
{txt}H0: diff = 0                                     Degrees of freedom = {res}     182

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.2380         {txt}Pr(|T| > |t|) = {res}0.4761          {txt}Pr(T > t) = {res}0.7620
{txt}
{com}. bysort  pct_goldenshare: su goldenshare

{txt}{hline}
-> pct_goldenshare = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}goldenshare {c |}{res}        111           0           0          0          0

{txt}{hline}
-> pct_goldenshare = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}goldenshare {c |}{res}         98    .1374351    .0705072   .0384615   .2272727

{txt}{hline}
-> pct_goldenshare = 3

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}goldenshare {c |}{res}         94     .255201    .0218226   .2333333   .3023256

{txt}{hline}
-> pct_goldenshare = 4

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}goldenshare {c |}{res}         90    .3671143    .0466168   .3055556   .5757576

{txt}
{com}. 
. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect  (count) n=partyeffect , by(pct_goldenshare)
{res}{txt}
{com}. 
. * upper and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}. 
. generate graphvar = 1  if pct_goldenshare == 1
{txt}(3 missing values generated)

{com}. replace  graphvar = 3 if pct_goldenshare == 2
{txt}(1 real change made)

{com}. replace  graphvar = 5 if pct_goldenshare == 3
{txt}(1 real change made)

{com}. replace  graphvar = 7 if pct_goldenshare == 4
{txt}(1 real change made)

{com}. 
. twoway (bar meanvar graphvar  , lcolor(black) graphregion(fcolor(white) ) bargap(100)  fintensity(0) plotregion(color(white) ) xlabel(, valuelabel angle(horizontal) nogrid) ///
>         xlabel(1 "0%" 3 "1-23%" 5 "23-30%" 7 ">30%", valuelabel angle(horizontal) nogrid)  ytitle("% Direct party effect")  title("Time periods", size(medium)) /// 
>          ylabel(0(0.2)0.8, nogrid) xsize(4) ysize(4) xtitle(% of Golden age/TS,size(medsmall))legend(off) name(gr1a, replace) ///
>           )   ///
>          (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black)) ///
>          (rcap hivar lowar graphvar, lcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid)) //
{res}{txt}
{com}.          
. cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export Fig_1.png, replace width(3600)
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_1.png{rm}
saved as
PNG
format
{p_end}

{com}. graph export Fig_1.tif, replace width(3600)
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_1.tif{rm}
saved as
TIFF
format
{p_end}

{com}. cd  "$DATADIR"   
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *Figure 2: Measure of inequality and partisan effects on inequality
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. bysort ineq_meas: su partyeffect

{txt}{hline}
-> ineq_meas = Gini

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        188    .3510638    .4785774          0          1

{txt}{hline}
-> ineq_meas = Ratio

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        110    .4090909    .4939163          0          1

{txt}{hline}
-> ineq_meas = Share

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         95    .3789474    .4876986          0          1

{txt}
{com}. 
. ttest partyeffect if ineq_meas <3, by(ineq_meas) //n.s.

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
    Gini {c |}{res}{col 12}    188{col 22} .3510638{col 34} .0349038{col 46} .4785774{col 58} .2822079{col 70} .4199197
   {txt}Ratio {c |}{res}{col 12}    110{col 22} .4090909{col 34} .0470931{col 46} .4939163{col 58}  .315754{col 70} .5024278
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    298{col 22} .3724832{col 34} .0280535{col 46} .4842793{col 58} .3172743{col 70} .4276921
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0580271{col 34} .0581342{col 58}-.1724358{col 70} .0563816
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Gini{txt}) - mean({res}Ratio{txt})                               t = {res} -0.9982
{txt}H0: diff = 0                                     Degrees of freedom = {res}     296

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.1595         {txt}Pr(|T| > |t|) = {res}0.3190          {txt}Pr(T > t) = {res}0.8405
{txt}
{com}. ttest partyeffect if ineq_meas !=2, by(ineq_meas) //n.s.

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
    Gini {c |}{res}{col 12}    188{col 22} .3510638{col 34} .0349038{col 46} .4785774{col 58} .2822079{col 70} .4199197
   {txt}Share {c |}{res}{col 12}     95{col 22} .3789474{col 34} .0500368{col 46} .4876986{col 58} .2795981{col 70} .4782967
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    283{col 22}  .360424{col 34} .0285909{col 46}  .480974{col 58} .3041453{col 70} .4167028
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0278835{col 34} .0606292{col 58}-.1472287{col 70} .0914616
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Gini{txt}) - mean({res}Share{txt})                               t = {res} -0.4599
{txt}H0: diff = 0                                     Degrees of freedom = {res}     281

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.3230         {txt}Pr(|T| > |t|) = {res}0.6459          {txt}Pr(T > t) = {res}0.6770
{txt}
{com}. ttest partyeffect if ineq_meas !=1, by(ineq_meas) //n.s.

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
   Ratio {c |}{res}{col 12}    110{col 22} .4090909{col 34} .0470931{col 46} .4939163{col 58}  .315754{col 70} .5024278
   {txt}Share {c |}{res}{col 12}     95{col 22} .3789474{col 34} .0500368{col 46} .4876986{col 58} .2795981{col 70} .4782967
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    205{col 22}  .395122{col 34} .0342282{col 46} .4900736{col 58} .3276355{col 70} .4626084
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0301435{col 34} .0687768{col 58}-.1054649{col 70}  .165752
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Ratio{txt}) - mean({res}Share{txt})                              t = {res}  0.4383
{txt}H0: diff = 0                                     Degrees of freedom = {res}     203

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.6692         {txt}Pr(|T| > |t|) = {res}0.6616          {txt}Pr(T > t) = {res}0.3308
{txt}
{com}. 
. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect (count) n=partyeffect  , by(ineq_meas)
{res}{txt}
{com}. 
. *upper and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}. 
. generate graphvar = ineq_meas    if ineq_meas == 1
{txt}(2 missing values generated)

{com}. replace  graphvar = ineq_meas+1  if ineq_meas == 2
{txt}(1 real change made)

{com}. replace  graphvar = ineq_meas+2 if ineq_meas == 3
{txt}(1 real change made)

{com}. 
. 
. twoway (bar meanvar graphvar, lcolor(black) graphregion(fcolor(white) ) bargap(200)  fintensity(0) plotregion(color(white) ) ///
>         yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid)  ///
>         xlabel(1 "Gini" 3 "Ratio" 5 "Share", valuelabel angle(45) nogrid) ytitle("% Direct party effect") ///
>         title("a)", size(medsmall)) xtitle(Measurement of inequality, size(medsmall))legend(off) name(gr2a, replace) ) ///
>          (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black) )  ///
>          (rcap hivar lowar graphvar, lcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid))  
{res}{txt}
{com}.         
. *Fig 2b: Gini:
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. bysort gini_gr: su partyeffect

{txt}{hline}
-> gini_gr = Pre-tax

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         65    .1846154    .3910046          0          1

{txt}{hline}
-> gini_gr = Post-tax

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         94    .4042553    .4933787          0          1

{txt}{hline}
-> gini_gr = Pre vs. post

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         29    .5517241    .5061202          0          1

{txt}{hline}
-> gini_gr = .

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        205     .395122    .4900736          0          1

{txt}
{com}. ttest partyeffect if gini_gr<3, by(gini_gr) //Pre vs. post: 0.005

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
 Pre-tax {c |}{res}{col 12}     65{col 22} .1846154{col 34} .0484982{col 46} .3910046{col 58} .0877292{col 70} .2815016
{txt}Post-tax {c |}{res}{col 12}     94{col 22} .4042553{col 34} .0508881{col 46} .4933787{col 58} .3032016{col 70} .5053091
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    159{col 22} .3144654{col 34} .0369379{col 46} .4657696{col 58} .2415096{col 70} .3874212
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.2196399{col 34} .0733085{col 58} -.364438{col 70}-.0748418
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Pre-tax{txt}) - mean({res}Post-tax{txt})                         t = {res} -2.9961
{txt}H0: diff = 0                                     Degrees of freedom = {res}     157

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0016         {txt}Pr(|T| > |t|) = {res}0.0032          {txt}Pr(T > t) = {res}0.9984
{txt}
{com}. ttest partyeffect if gini_gr!=2, by(gini_gr) //Pre vs. prevspost: 0.0001

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
 Pre-tax {c |}{res}{col 12}     65{col 22} .1846154{col 34} .0484982{col 46} .3910046{col 58} .0877292{col 70} .2815016
 {txt}Pre vs. {c |}{res}{col 12}     29{col 22} .5517241{col 34} .0939842{col 46} .5061202{col 58} .3592063{col 70}  .744242
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}     94{col 22} .2978723{col 34} .0474222{col 46} .4597752{col 58} .2037013{col 70} .3920434
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.3671088{col 34} .0958714{col 58}-.5575177{col 70}-.1766998
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Pre-tax{txt}) - mean({res}Pre vs.{txt})                          t = {res} -3.8292
{txt}H0: diff = 0                                     Degrees of freedom = {res}      92

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0001         {txt}Pr(|T| > |t|) = {res}0.0002          {txt}Pr(T > t) = {res}0.9999
{txt}
{com}. 
. clonevar gini_d=gini_gr
{txt}(205 missing values generated)

{com}. recode gini_d(3=2)
{txt}(29 changes made to {bf:gini_d})

{com}. ttest partyeffect if gini_gr!=2, by(gini_d) //Pre vs. post/prepost: 0.0001

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
 Pre-tax {c |}{res}{col 12}     65{col 22} .1846154{col 34} .0484982{col 46} .3910046{col 58} .0877292{col 70} .2815016
{txt}Post-tax {c |}{res}{col 12}     29{col 22} .5517241{col 34} .0939842{col 46} .5061202{col 58} .3592063{col 70}  .744242
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}     94{col 22} .2978723{col 34} .0474222{col 46} .4597752{col 58} .2037013{col 70} .3920434
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.3671088{col 34} .0958714{col 58}-.5575177{col 70}-.1766998
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Pre-tax{txt}) - mean({res}Post-tax{txt})                         t = {res} -3.8292
{txt}H0: diff = 0                                     Degrees of freedom = {res}      92

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0001         {txt}Pr(|T| > |t|) = {res}0.0002          {txt}Pr(T > t) = {res}0.9999
{txt}
{com}. 
. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect (count) n=partyeffect, by(gini_gr)
{res}{txt}
{com}. 
. * and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}. 
. generate graphvar = 1  if gini_gr == 1
{txt}(3 missing values generated)

{com}. replace  graphvar = 5 if gini_gr == 2
{txt}(1 real change made)

{com}. replace  graphvar = 3 if gini_gr == 3
{txt}(1 real change made)

{com}. 
. twoway (bar meanvar graphvar, lcolor(black) graphregion(fcolor(white) ) bargap(100)  fintensity(0) plotregion(color(white) ) xlabel(, valuelabel angle(horizontal) nogrid) ///
>         xlabel(1 " Pre " 3 " Diff. " 5 " Post", valuelabel angle(45) nogrid)  ytitle("% Direct party effect") /// 
>         title("b)", size(medsmall))  ylabel(0(0.2)0.8, nogrid)  xtitle(Gini,size(medsmall))legend(off))  ///
>          (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black) name(gr2b, replace))  ///
>          (rcap hivar lowar graphvar, lcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid))   
{res}{txt}
{com}.          
. 
. *Fig2c: Top-income vs. rest
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. bysort toprest: su partyeffect

{txt}{hline}
-> toprest = 0

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        212    .3207547    .4678714          0          1

{txt}{hline}
-> toprest = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        181    .4364641    .4973225          0          1

{txt}
{com}. ttest partyeffect, by(toprest) level(90)  // sign.

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[90% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    212{col 22} .3207547{col 34} .0321335{col 46} .4678714{col 58} .2676667{col 70} .3738428
       {txt}1 {c |}{res}{col 12}    181{col 22} .4364641{col 34} .0369657{col 46} .4973225{col 58} .3753464{col 70} .4975818
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    393{col 22} .3740458{col 34} .0244394{col 46} .4844923{col 58} .3337513{col 70} .4143403
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.1157094{col 34} .0487443{col 58} -.196077{col 70}-.0353418
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} -2.3738
{txt}H0: diff = 0                                     Degrees of freedom = {res}     391

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0090         {txt}Pr(|T| > |t|) = {res}0.0181          {txt}Pr(T > t) = {res}0.9910
{txt}
{com}. 
. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect (count) n=partyeffect, by(toprest)
{res}{txt}
{com}. 
. * upper and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}. 
. generate graphvar = 2  if toprest == 1
{txt}(1 missing value generated)

{com}. replace  graphvar = 4 if toprest == 0
{txt}(1 real change made)

{com}. 
. twoway (bar meanvar graphvar, lcolor(black) graphregion(fcolor(white) ) bargap(0)  fintensity(0) plotregion(color(white) ) xlabel(, valuelabel angle(horizontal) nogrid) ///
>          xlabel(2 " Top" 4 " Rest", valuelabel angle(45) nogrid)  ytitle("% Direct party effect")   /// 
>          ylabel(0(0.2)0.8, nogrid) title("c)", size(medsmall)) xtitle(Top income vs. Rest,size(medsmall))legend(off)) ///
>         (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black) name(gr2c, replace))  ///
>          (rcap hivar lowar graphvar, lcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid))
{res}{txt}
{com}.          
. 
. graph combine gr2a gr2b gr2c, col(3) ycommon xcommon title("Measure of inequality", size(medium)) ///
>         ysize(3) xsize(4) 
{res}{txt}
{com}. cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export Fig_2.png, replace width(3600)
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_2.png{rm}
saved as
PNG
format
{p_end}

{com}. graph export Fig_2.tif, replace width(3600)
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_2.tif{rm}
saved as
TIFF
format
{p_end}

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *Figure 3: Conceptualization of partisan effects on inequality
. 
. *Fig 3a: Cumulative measure
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. bysort ideol_cum: su partyeffect

{txt}{hline}
-> ideol_cum = 0

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        198    .2474747    .4326388          0          1

{txt}{hline}
-> ideol_cum = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        195    .5025641    .5012804          0          1

{txt}
{com}. ttest partyeffect, by(ideol_cum)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    198{col 22} .2474747{col 34} .0307463{col 46} .4326388{col 58} .1868406{col 70} .3081089
       {txt}1 {c |}{res}{col 12}    195{col 22} .5025641{col 34} .0358974{col 46} .5012804{col 58} .4317648{col 70} .5733634
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    393{col 22} .3740458{col 34} .0244394{col 46} .4844923{col 58} .3259971{col 70} .4220945
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.2550894{col 34} .0472119{col 58}-.3479104{col 70}-.1622683
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} -5.4031
{txt}H0: diff = 0                                     Degrees of freedom = {res}     391

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect (count) n=partyeffect  , by(ideol_cum)
{res}{txt}
{com}. 
. * upper and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}.         
. generate graphvar = 2    if ideol_cum == 0
{txt}(1 missing value generated)

{com}. replace  graphvar = 4  if ideol_cum == 1
{txt}(1 real change made)

{com}. 
. twoway (bar meanvar graphvar, lcolor(black) graphregion(fcolor(white) ) bargap(200)  fintensity(0) plotregion(color(white) ) ///
>         yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid) xsize(4) ysize(6) ///
>         xlabel(2 "No" 4 "Yes", valuelabel angle(horizontal) nogrid) ytitle("% Direct party effect") ///
>         title("a)", size(medium)) xtitle("Cumulative partisan effects", size(medsmall))legend(off) name(gr3a, replace) ) ///
>          (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black) ) ///
>          (rcap hivar lowar graphvar, alcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid))
{res}{txt}
{com}. 
. *Fig.3b) IV vs. DV
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. bysort party_control: su partyeffect

{txt}{hline}
-> party_control = no

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        173    .4739884    .5007724          0          1

{txt}{hline}
-> party_control = yes

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        220    .2954545    .4572873          0          1

{txt}
{com}. ttest partyeffect, by(party_control)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
      no {c |}{res}{col 12}    173{col 22} .4739884{col 34}  .038073{col 46} .5007724{col 58} .3988379{col 70} .5491389
     {txt}yes {c |}{res}{col 12}    220{col 22} .2954545{col 34} .0308303{col 46} .4572873{col 58} .2346925{col 70} .3562166
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    393{col 22} .3740458{col 34} .0244394{col 46} .4844923{col 58} .3259971{col 70} .4220945
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1785339{col 34} .0484612{col 58} .0832569{col 70} .2738109
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}no{txt}) - mean({res}yes{txt})                                   t = {res}  3.6841
{txt}H0: diff = 0                                     Degrees of freedom = {res}     391

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9999         {txt}Pr(|T| > |t|) = {res}0.0003          {txt}Pr(T > t) = {res}0.0001
{txt}
{com}. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect (count) n=partyeffect  , by(party_control)
{res}{txt}
{com}. 
. * upper and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}.         
. generate graphvar = 2   if party_control == 1
{txt}(1 missing value generated)

{com}. replace  graphvar = 4  if party_control == 0
{txt}(1 real change made)

{com}. 
. 
. twoway (bar meanvar graphvar, lcolor(black) graphregion(fcolor(white) ) bargap(200)  fintensity(0) plotregion(color(white) ) ///
>         yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid) xsize(4) ysize(6) ///
>         xlabel(4 "IV" 2 "Control", valuelabel angle(horizontal) nogrid)  ytitle("% Direct party effect") ///
>         title("b)", size(medium)) xtitle("Partisan effects as...", size(medsmall))legend(off) name(gr3b, replace) ) ///
>          (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black) )  ///
>          (rcap hivar lowar graphvar, lcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid))
{res}{txt}
{com}.                  
. 
. graph combine gr3a gr3b, ycommon xcommon row(1) ysize(4) xsize(6) ///
>         title("Conceptualization and status of partisan effects", size(medium)) //
{res}{txt}
{com}. cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export Fig_3_old.png, replace width(3600)
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_3_old.png{rm}
saved as
PNG
format
{p_end}

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *Fig.3c) All controls
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. bysort controls_total: su partyeffect

{txt}{hline}
-> controls_total = 0

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}          2           0           0          0          0

{txt}{hline}
-> controls_total = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         14          .5    .5188745          0          1

{txt}{hline}
-> controls_total = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         18    .6111111    .5016313          0          1

{txt}{hline}
-> controls_total = 3

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         38    .4736842    .5060094          0          1

{txt}{hline}
-> controls_total = 4

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         40          .4    .4961389          0          1

{txt}{hline}
-> controls_total = 5

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         89    .4269663    .4974398          0          1

{txt}{hline}
-> controls_total = 6

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        102    .3431373    .4771014          0          1

{txt}{hline}
-> controls_total = 7

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         28    .3214286    .4755949          0          1

{txt}{hline}
-> controls_total = 8

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         62    .2096774    .4104015          0          1

{txt}
{com}. su controls_total

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
controls_t~l {c |}{res}        393    5.274809    1.869683          0          8
{txt}
{com}. su controls_total,d

                  {txt}Total number of controls
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              0
{txt} 5%    {res}        2              0
{txt}10%    {res}        3              1       {txt}Obs         {res}        393
{txt}25%    {res}        4              1       {txt}Sum of wgt. {res}        393

{txt}50%    {res}        5                      {txt}Mean          {res} 5.274809
                        {txt}Largest       Std. dev.     {res} 1.869683
{txt}75%    {res}        6              8
{txt}90%    {res}        8              8       {txt}Variance      {res} 3.495716
{txt}95%    {res}        8              8       {txt}Skewness      {res}-.4353152
{txt}99%    {res}        8              8       {txt}Kurtosis      {res} 2.744473
{txt}
{com}. 
. xtile controls_total_3=controls_total, nq(2)
{txt}
{com}. bysort controls_total_3: su controls_total

{txt}{hline}
-> controls_total_3 = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
controls_t~l {c |}{res}        201    3.825871    1.324586          0          5

{txt}{hline}
-> controls_total_3 = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
controls_t~l {c |}{res}        192    6.791667    .9027783          6          8

{txt}
{com}. 
. ttest partyeffect, by(controls_total_3) //44% vs. 30%

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}    201{col 22} .4477612{col 34} .0351618{col 46} .4985052{col 58} .3784257{col 70} .5170967
       {txt}2 {c |}{res}{col 12}    192{col 22}  .296875{col 34} .0330587{col 46} .4580754{col 58} .2316679{col 70} .3620821
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    393{col 22} .3740458{col 34} .0244394{col 46} .4844923{col 58} .3259971{col 70} .4220945
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1508862{col 34} .0483558{col 58} .0558164{col 70}  .245956
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res}  3.1203
{txt}H0: diff = 0                                     Degrees of freedom = {res}     391

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9990         {txt}Pr(|T| > |t|) = {res}0.0019          {txt}Pr(T > t) = {res}0.0010
{txt}
{com}. 
. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect (count) n=partyeffect  , by(controls_total_3)
{res}{txt}
{com}. 
. * upper and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}.         
. generate graphvar = 2   if controls_total_3 == 1
{txt}(1 missing value generated)

{com}. replace  graphvar = 4  if controls_total_3 == 2
{txt}(1 real change made)

{com}. 
. twoway (bar meanvar graphvar, lcolor(black) graphregion(fcolor(white) ) bargap(200)  fintensity(0) plotregion(color(white) ) ///
>         yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid)  ///
>         xlabel(2 "0-5" 4 "6-8", valuelabel angle(horizontal) nogrid)  ytitle("% Direct party effect") ///
>         title("c)", size(medium)) xtitle("Number of policy channels", size(medsmall)) legend(off) name(gr3c, replace) ) ///
>          (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black) )  ///
>          (rcap hivar lowar graphvar, lcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid))
{res}{txt}
{com}.                  
. *Combined graph:
. graph combine gr3a gr3b gr3c, ycommon xcommon row(1) ysize(3) xsize(4) ///
>         title("Conceptualization and status of partisan effects", size(medium)) //
{res}{txt}
{com}. cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export Fig_3.png, replace width(3600)
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_3.png{rm}
saved as
PNG
format
{p_end}

{com}. graph export Fig_3.tif, replace width(3600)
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_3.tif{rm}
saved as
TIFF
format
{p_end}

{com}. 
. cd  "$DATADIR"   
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *3) Regression analysis
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. *3a) Table 1
. 
. melogit partyeffect c.goldenshare ib0.party_control i.ineq_meas   ib0.ideol_cum  controls_total  c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-213.87763}  
Iteration 1:{space 2}Log likelihood = {res:-213.20556}  
Iteration 2:{space 2}Log likelihood = {res: -213.2045}  
Iteration 3:{space 2}Log likelihood = {res: -213.2045}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-177.85851}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-177.85851}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -169.6397}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-167.63009}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-167.37068}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-167.36746}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-167.36774}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-167.36777}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-167.36777}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    27.68
{txt}Log pseudolikelihood = {res}-167.36777{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0005
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-3.376713{col 26}{space 2}  3.05442{col 37}{space 1}   -1.11{col 46}{space 3}0.269{col 54}{space 4}-9.363268{col 67}{space 3} 2.609841
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2} -1.98017{col 26}{space 2} .9760174{col 37}{space 1}   -2.03{col 46}{space 3}0.042{col 54}{space 4}-3.893129{col 67}{space 3}-.0672106
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .4640449{col 26}{space 2}  .366476{col 37}{space 1}    1.27{col 46}{space 3}0.205{col 54}{space 4}-.2542349{col 67}{space 3} 1.182325
{txt}{space 6}Share  {c |}{col 14}{res}{space 2}  2.31473{col 26}{space 2} .9244469{col 37}{space 1}    2.50{col 46}{space 3}0.012{col 54}{space 4} .5028476{col 67}{space 3} 4.126613
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2}   3.1677{col 26}{space 2} 1.037616{col 37}{space 1}    3.05{col 46}{space 3}0.002{col 54}{space 4}  1.13401{col 67}{space 3}  5.20139
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2693911{col 26}{space 2}  .149748{col 37}{space 1}   -1.80{col 46}{space 3}0.072{col 54}{space 4}-.5628918{col 67}{space 3} .0241096
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6744827{col 26}{space 2} .3458302{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-1.352298{col 67}{space 3} .0033321
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0040931{col 26}{space 2} .0024814{col 37}{space 1}    1.65{col 46}{space 3}0.099{col 54}{space 4}-.0007702{col 67}{space 3} .0089565
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0281546{col 26}{space 2}  1.36443{col 37}{space 1}   -0.02{col 46}{space 3}0.984{col 54}{space 4}-2.702387{col 67}{space 3} 2.646078
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2}  7.18499{col 26}{space 2} 3.607698{col 54}{space 4} 2.685495{col 67}{space 3}  19.2233
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1a
{txt}
{com}.  
.  est restore m1a
{txt}(results {stata estimates replay m1a:m1a} are active now)

{com}.  margins, over(ineq_meas) pwcompare level (90)
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:ineq_meas}{p_end}
{p2colreset}{...}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 17}{c |}{col 29} Delta-method{col 40}         Una{col 54}djusted
{col 17}{c |}   Contrast{col 29}   std. err.{col 41}     [90% con{col 54}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 6}ineq_meas {c |}
{space 1}Ratio vs Gini  {c |}{col 17}{res}{space 2} .0680594{col 29}{space 2} .0433171{col 40}{space 5} -.003191{col 54}{space 3} .1393097
{txt}{space 1}Share vs Gini  {c |}{col 17}{res}{space 2} .1589755{col 29}{space 2} .0873301{col 40}{space 5} .0153302{col 54}{space 3} .3026207
{txt}Share vs Ratio  {c |}{col 17}{res}{space 2} .0909161{col 29}{space 2} .0727935{col 40}{space 5}-.0288185{col 54}{space 3} .2106507
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}.  margins, over(ineq_meas) level (90)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:ineq_meas}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ineq_meas {c |}
{space 7}Gini  {c |}{col 14}{res}{space 2} .3424437{col 26}{space 2} .0560532{col 37}{space 1}    6.11{col 46}{space 3}0.000{col 54}{space 4} .2502445{col 67}{space 3}  .434643
{txt}{space 6}Ratio  {c |}{col 14}{res}{space 2} .4105031{col 26}{space 2} .0574395{col 37}{space 1}    7.15{col 46}{space 3}0.000{col 54}{space 4} .3160235{col 67}{space 3} .5049827
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} .5014192{col 26}{space 2} .0747102{col 37}{space 1}    6.71{col 46}{space 3}0.000{col 54}{space 4} .3785319{col 67}{space 3} .6243065
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
.  margins, over(ideol_cum)  level (90) //31 vs. 50%
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:ideol_cum}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ideol_cum {c |}
{space 10}0  {c |}{col 14}{res}{space 2} .3135691{col 26}{space 2} .0543581{col 37}{space 1}    5.77{col 46}{space 3}0.000{col 54}{space 4}  .224158{col 67}{space 3} .4029802
{txt}{space 10}1  {c |}{col 14}{res}{space 2} .4876047{col 26}{space 2} .0769564{col 37}{space 1}    6.34{col 46}{space 3}0.000{col 54}{space 4} .3610226{col 67}{space 3} .6141867
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.   margins, over(ideol_cum) pwcompare  level (90) //19%
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:ideol_cum}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method{col 37}         Una{col 51}djusted
{col 14}{c |}   Contrast{col 26}   std. err.{col 38}     [90% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}ideol_cum {c |}
{space 5}1 vs 0  {c |}{col 14}{res}{space 2} .1740356{col 26}{space 2} .0925912{col 37}{space 5} .0217367{col 51}{space 3} .3263345
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
.  margins, over(party_control) pwcompare level (90) //17%
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:party_control}{p_end}
{p2colreset}{...}

{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 15}{c |}{col 27} Delta-method{col 38}         Una{col 52}djusted
{col 15}{c |}   Contrast{col 27}   std. err.{col 39}     [90% con{col 52}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
party_control {c |}
{space 3}yes vs no  {c |}{col 15}{res}{space 2} -.180477{col 27}{space 2} .0920073{col 38}{space 5}-.3318157{col 52}{space 3}-.0291384
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}.  margins, dydx(goldenshare) pwcompare level (90) //23%
{txt}{p 0 6 2}note: ignoring pwcompare options because there are no margins for making pairwise comparisons.{p_end}
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:goldenshare}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-.3611203{col 26}{space 2} .3237301{col 37}{space 1}   -1.12{col 46}{space 3}0.265{col 54}{space 4}-.8936091{col 67}{space 3} .1713684
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. melogit partyeffect c.goldenshare toprest  ib0.ideol_cum ib0.party_control controls_total c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-210.06493}  
Iteration 1:{space 2}Log likelihood = {res:-209.47081}  
Iteration 2:{space 2}Log likelihood = {res:-209.46976}  
Iteration 3:{space 2}Log likelihood = {res:-209.46976}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-173.59674}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-173.59674}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-164.39458}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-161.12258}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-160.20778}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-160.12978}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-160.12905}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-160.12901}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-160.12901}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    18.46
{txt}Log pseudolikelihood = {res}-160.12901{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0101
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.261786{col 26}{space 2} 2.912911{col 37}{space 1}   -0.78{col 46}{space 3}0.437{col 54}{space 4}-7.970987{col 67}{space 3} 3.447416
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} 2.343037{col 26}{space 2} 1.081403{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .2235263{col 67}{space 3} 4.462547
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.215504{col 26}{space 2} 1.077868{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} 1.102922{col 67}{space 3} 5.328085
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.966817{col 26}{space 2} 1.073582{col 37}{space 1}   -1.83{col 46}{space 3}0.067{col 54}{space 4}-4.070999{col 67}{space 3} .1373662
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3404235{col 26}{space 2} .1654117{col 37}{space 1}   -2.06{col 46}{space 3}0.040{col 54}{space 4}-.6646245{col 67}{space 3}-.0162225
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6569371{col 26}{space 2} .3522677{col 37}{space 1}   -1.86{col 46}{space 3}0.062{col 54}{space 4}-1.347369{col 67}{space 3} .0334948
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0044522{col 26}{space 2} .0024775{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4}-.0004037{col 67}{space 3} .0093081
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.3659238{col 26}{space 2}  1.43627{col 37}{space 1}   -0.25{col 46}{space 3}0.799{col 54}{space 4} -3.18096{col 67}{space 3} 2.449113
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.247118{col 26}{space 2} 4.606258{col 54}{space 4} 2.759822{col 67}{space 3} 24.64469
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2a
{txt}
{com}.  est restore m2a
{txt}(results {stata estimates replay m2a:m2a} are active now)

{com}.  margins, over(toprest) pwcompare
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:toprest}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method{col 37}         Una{col 51}djusted
{col 14}{c |}   Contrast{col 26}   std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 5}toprest {c |}
{space 5}1 vs 0  {c |}{col 14}{res}{space 2} .1948384{col 26}{space 2} .0843352{col 37}{space 5} .0295445{col 51}{space 3} .3601322
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}.  margins, dydx(toprest) 
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:toprest}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}toprest {c |}{col 14}{res}{space 2} .2273616{col 26}{space 2} .0835094{col 37}{space 1}    2.72{col 46}{space 3}0.006{col 54}{space 4} .0636861{col 67}{space 3}  .391037
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  
.   margins, over(ideol_cum)  level (90) //30 vs. 52%
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:ideol_cum}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ideol_cum {c |}
{space 10}0  {c |}{col 14}{res}{space 2} .3088807{col 26}{space 2}  .051582{col 37}{space 1}    5.99{col 46}{space 3}0.000{col 54}{space 4} .2240359{col 67}{space 3} .3937255
{txt}{space 10}1  {c |}{col 14}{res}{space 2} .5068862{col 26}{space 2}  .069139{col 37}{space 1}    7.33{col 46}{space 3}0.000{col 54}{space 4} .3931625{col 67}{space 3} .6206098
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.   margins, over(ideol_cum) pwcompare  level (90) //22%
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:ideol_cum}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method{col 37}         Una{col 51}djusted
{col 14}{c |}   Contrast{col 26}   std. err.{col 38}     [90% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}ideol_cum {c |}
{space 5}1 vs 0  {c |}{col 14}{res}{space 2} .1980054{col 26}{space 2} .0836948{col 37}{space 5} .0603397{col 51}{space 3} .3356712
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}.  
.   margins, over(party_control)  level (90) //34-52%
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:party_control}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
party_cont~l {c |}
{space 9}no  {c |}{col 14}{res}{space 2}  .506854{col 26}{space 2} .0659648{col 37}{space 1}    7.68{col 46}{space 3}0.000{col 54}{space 4} .3983515{col 67}{space 3} .6153566
{txt}{space 8}yes  {c |}{col 14}{res}{space 2} .3287065{col 26}{space 2} .0576993{col 37}{space 1}    5.70{col 46}{space 3}0.000{col 54}{space 4} .2337996{col 67}{space 3} .4236135
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.   margins, over(party_control) pwcompare level (90) //17% 
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:party_control}{p_end}
{p2colreset}{...}

{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 15}{c |}{col 27} Delta-method{col 38}         Una{col 52}djusted
{col 15}{c |}   Contrast{col 27}   std. err.{col 39}     [90% con{col 52}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
party_control {c |}
{space 3}yes vs no  {c |}{col 15}{res}{space 2}-.1781475{col 27}{space 2} .0858903{col 38}{space 5}-.3194245{col 52}{space 3}-.0368706
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}.   
.     margins, at((p25) controls_total) at((p75) controls_total) //39-45
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 14:controls_total} = {res:{ralign 1:4}} {txt:(p25)}
{lalign 7:2._at: }{space 0}{lalign 14:controls_total} = {res:{ralign 1:6}} {txt:(p75)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4501142{col 26}{space 2} .0493817{col 37}{space 1}    9.11{col 46}{space 3}0.000{col 54}{space 4} .3533278{col 67}{space 3} .5469006
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3810504{col 26}{space 2} .0488033{col 37}{space 1}    7.81{col 46}{space 3}0.000{col 54}{space 4} .2853978{col 67}{space 3}  .476703
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.     margins, at((p25) controls_total) at((p75) controls_total) pwcompare //-7
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 14:controls_total} = {res:{ralign 1:4}} {txt:(p25)}
{lalign 7:2._at: }{space 0}{lalign 14:controls_total} = {res:{ralign 1:6}} {txt:(p75)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method{col 37}         Una{col 51}djusted
{col 14}{c |}   Contrast{col 26}   std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 5}2 vs 1  {c |}{col 14}{res}{space 2}-.0690638{col 26}{space 2} .0342627{col 37}{space 5}-.1362174{col 51}{space 3}-.0019101
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}.     margins, at((min) controls_total) at((max) controls_total) //32-59 (27%)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 14:controls_total} = {res:{ralign 1:0}} {txt:(min)}
{lalign 7:2._at: }{space 0}{lalign 14:controls_total} = {res:{ralign 1:8}} {txt:(max)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .5914944{col 26}{space 2} .0947308{col 37}{space 1}    6.24{col 46}{space 3}0.000{col 54}{space 4} .4058254{col 67}{space 3} .7771635
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3165268{col 26}{space 2} .0641921{col 37}{space 1}    4.93{col 46}{space 3}0.000{col 54}{space 4} .1907127{col 67}{space 3}  .442341
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.     margins, at((min) controls_total) at((max) controls_total) pwcompare //33-59 (27%)
{res}
{txt}{col 1}Pairwise comparisons of predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 14:controls_total} = {res:{ralign 1:0}} {txt:(min)}
{lalign 7:2._at: }{space 0}{lalign 14:controls_total} = {res:{ralign 1:8}} {txt:(max)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method{col 37}         Una{col 51}djusted
{col 14}{c |}   Contrast{col 26}   std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 5}2 vs 1  {c |}{col 14}{res}{space 2}-.2749676{col 26}{space 2} .1319297{col 37}{space 5} -.533545{col 51}{space 3}-.0163902
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
.  
. melogit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-90.987765}  
Iteration 1:{space 2}Log likelihood = {res:-88.248259}  
Iteration 2:{space 2}Log likelihood = {res:-88.219698}  
Iteration 3:{space 2}Log likelihood = {res:-88.219697}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-81.648807}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-81.648807}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-80.188917}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-79.955931}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-79.946748}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-79.946731}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-79.946731}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       188
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.8
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    39.48
{txt}Log pseudolikelihood = {res}-79.946731{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-4.550399{col 26}{space 2} 4.236364{col 37}{space 1}   -1.07{col 46}{space 3}0.283{col 54}{space 4}-12.85352{col 67}{space 3} 3.752722
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .7103867{col 26}{space 2} 1.269799{col 37}{space 1}    0.56{col 46}{space 3}0.576{col 54}{space 4}-1.778374{col 67}{space 3} 3.199147
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} 1.000453{col 26}{space 2} .7109585{col 37}{space 1}    1.41{col 46}{space 3}0.159{col 54}{space 4}-.3930002{col 67}{space 3} 2.393906
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.065932{col 26}{space 2} 1.462413{col 37}{space 1}    1.41{col 46}{space 3}0.158{col 54}{space 4}-.8003457{col 67}{space 3} 4.932209
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-3.612916{col 26}{space 2} 1.872056{col 37}{space 1}   -1.93{col 46}{space 3}0.054{col 54}{space 4}-7.282078{col 67}{space 3}  .056246
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3193909{col 26}{space 2} .2077367{col 37}{space 1}   -1.54{col 46}{space 3}0.124{col 54}{space 4}-.7265473{col 67}{space 3} .0877654
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.3999904{col 26}{space 2}  .860415{col 37}{space 1}   -0.46{col 46}{space 3}0.642{col 54}{space 4}-2.086373{col 67}{space 3} 1.286392
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0084538{col 26}{space 2}  .004761{col 37}{space 1}    1.78{col 46}{space 3}0.076{col 54}{space 4}-.0008775{col 67}{space 3} .0177851
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1457716{col 26}{space 2} 2.072666{col 37}{space 1}    0.07{col 46}{space 3}0.944{col 54}{space 4} -3.91658{col 67}{space 3} 4.208123
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 3.139942{col 26}{space 2} 2.371739{col 54}{space 4} .7144483{col 67}{space 3} 13.79979
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3a 
{txt}
{com}.   est restore m3a
{txt}(results {stata estimates replay m3a:m3a} are active now)

{com}.  margins, over(gini_gr) level(90)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:188}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:gini_gr}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}gini_gr {c |}
{space 4}Pre-tax  {c |}{col 14}{res}{space 2} .2205394{col 26}{space 2} .0750548{col 37}{space 1}    2.94{col 46}{space 3}0.003{col 54}{space 4} .0970852{col 67}{space 3} .3439935
{txt}{space 3}Post-tax  {c |}{col 14}{res}{space 2}  .335329{col 26}{space 2} .1007812{col 37}{space 1}    3.33{col 46}{space 3}0.001{col 54}{space 4} .1695586{col 67}{space 3} .5010994
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .5454557{col 26}{space 2} .1066492{col 37}{space 1}    5.11{col 46}{space 3}0.000{col 54}{space 4} .3700334{col 67}{space 3}  .720878
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
.  cd  "$RESULTSDIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a m3a m2a using Table_1.rtf, label replace  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "Model 1" "Model 2" "Model 3") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff."  ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age"  ///
>   controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest  *ideol_cum *party_control *controls_total)
{res}{txt}(output written to {browse  `"Table_1.rtf"'})

{com}. cd  "$DATADIR"  
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *Figure 4: Predicted probabilities plot
. 
. gen graph_id=_n/2 in 1/38
{txt}(355 missing values generated)

{com}. gen graph_x=graph_id-1
{txt}(355 missing values generated)

{com}. 
. gen effect=.
{txt}(393 missing values generated)

{com}. gen upper=.
{txt}(393 missing values generated)

{com}. gen lower=.
{txt}(393 missing values generated)

{com}. 
. *Golden share
. est restore m1a
{txt}(results {stata estimates replay m1a:m1a} are active now)

{com}. margins,at((min)goldenshare)   post level (90) //45%
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 11:goldenshare} = {res:{ralign 1:0}} {txt:(min)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4649402{col 26}{space 2} .0672369{col 37}{space 1}    6.91{col 46}{space 3}0.000{col 54}{space 4} .3543453{col 67}{space 3} .5755351
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
       _cons
r1 {res} .4649402
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
          _cons
_cons {res} .0045208
{reset}
{com}. 
. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. est restore m1a
{txt}(results {stata estimates replay m1a:m1a} are active now)

{com}. margins,at( (max)goldenshare) post level (90) //38
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 11:goldenshare} = {res:{ralign 8:.5757576}} {txt:(max)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .2731316{col 26}{space 2} .1140838{col 37}{space 1}    2.39{col 46}{space 3}0.017{col 54}{space 4} .0854804{col 67}{space 3} .4607827
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .27313156
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .01301512
{reset}
{com}. 
. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+1
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+1
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+1
{txt}  6{com}. {c )-}  
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}.   
.   
. est restore m1a
{txt}(results {stata estimates replay m1a:m1a} are active now)

{com}.   
. margins, over(ineq_meas) post level (90)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:ineq_meas}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ineq_meas {c |}
{space 7}Gini  {c |}{col 14}{res}{space 2} .3424437{col 26}{space 2} .0560532{col 37}{space 1}    6.11{col 46}{space 3}0.000{col 54}{space 4} .2502445{col 67}{space 3}  .434643
{txt}{space 6}Ratio  {c |}{col 14}{res}{space 2} .4105031{col 26}{space 2} .0574395{col 37}{space 1}    7.15{col 46}{space 3}0.000{col 54}{space 4} .3160235{col 67}{space 3} .5049827
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} .5014192{col 26}{space 2} .0747102{col 37}{space 1}    6.71{col 46}{space 3}0.000{col 54}{space 4} .3785319{col 67}{space 3} .6243065
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}b[1,3]
            1.         2.         3.
    ineq_meas  ineq_meas  ineq_meas
r1 {res} .34244375   .4105031   .5014192
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[3,3]
                     1.         2.         3.
             ineq_meas  ineq_meas  ineq_meas
1.ineq_meas {res} .00314196
{txt}2.ineq_meas {res} .00228244   .0032993
{txt}3.ineq_meas {res} .00054851  .00179101  .00558161
{reset}
{com}. 
. 
. forval i=1/3 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+2.5
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+2.5
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+2.5
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
.  *Gini
. est restore m3a
{txt}(results {stata estimates replay m3a:m3a} are active now)

{com}.  margins, over(gini_gr) level(90) 
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:188}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:gini_gr}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}gini_gr {c |}
{space 4}Pre-tax  {c |}{col 14}{res}{space 2} .2205394{col 26}{space 2} .0750548{col 37}{space 1}    2.94{col 46}{space 3}0.003{col 54}{space 4} .0970852{col 67}{space 3} .3439935
{txt}{space 3}Post-tax  {c |}{col 14}{res}{space 2}  .335329{col 26}{space 2} .1007812{col 37}{space 1}    3.33{col 46}{space 3}0.001{col 54}{space 4} .1695586{col 67}{space 3} .5010994
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .5454557{col 26}{space 2} .1066492{col 37}{space 1}    5.11{col 46}{space 3}0.000{col 54}{space 4} .3700334{col 67}{space 3}  .720878
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}b[1,3]
            1.         2.         3.
      gini_gr    gini_gr    gini_gr
r1 {res} .22053935  .33532902   .5454557
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[3,3]
                   1.         2.         3.
             gini_gr    gini_gr    gini_gr
1.gini_gr {res} .00563322
{txt}2.gini_gr {res} .00048631  .01015686
{txt}3.gini_gr {res} .00433082  .00033736  .01137405
{reset}
{com}. forval i=1/3 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+6
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+6
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+6
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

{com}.  
. *Top vs. rest
.  est restore m2a 
{txt}(results {stata estimates replay m2a:m2a} are active now)

{com}.  margins, at(toprest=0) level(90) 
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 7:toprest} = {res:{ralign 1:0}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .3026994{col 26}{space 2} .0557371{col 37}{space 1}    5.43{col 46}{space 3}0.000{col 54}{space 4} .2110201{col 67}{space 3} .3943787
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .30269941
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00310662
{reset}
{com}. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+9.5
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+9.5
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+9.5
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
.  est restore m2a 
{txt}(results {stata estimates replay m2a:m2a} are active now)

{com}.  margins, at(toprest=1) level(90) 
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 7:toprest} = {res:{ralign 1:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5367301{col 26}{space 2} .0701774{col 37}{space 1}    7.65{col 46}{space 3}0.000{col 54}{space 4} .4212986{col 67}{space 3} .6521616
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .53673008
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00492486
{reset}
{com}. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+10.5
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+10.5
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+10.5
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. *Cumulative ms
.    est restore m2a
{txt}(results {stata estimates replay m2a:m2a} are active now)

{com}. margins, at(ideol_cum=0) level (90) //32%
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 9:ideol_cum} = {res:{ralign 1:0}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .2556993{col 26}{space 2} .0470354{col 37}{space 1}    5.44{col 46}{space 3}0.000{col 54}{space 4}  .178333{col 67}{space 3} .3330656
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .25569928
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00221233
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+12
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+12
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+12
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. est restore m2a
{txt}(results {stata estimates replay m2a:m2a} are active now)

{com}. margins, at(ideol_cum=1) level (90) //57%
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 9:ideol_cum} = {res:{ralign 1:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2}  .570559{col 26}{space 2} .0673786{col 37}{space 1}    8.47{col 46}{space 3}0.000{col 54}{space 4} .4597312{col 67}{space 3} .6813869
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .57055905
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00453987
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+13
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+13
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+13
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. *Control variable 
.    est restore m2a
{txt}(results {stata estimates replay m2a:m2a} are active now)

{com}.  margins, at(party_control=1) pwcompare level (90) //31%
{txt}{p 0 6 2}note: ignoring pwcompare options because there are no margins for making pairwise comparisons.{p_end}
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 13:party_control} = {res:{ralign 1:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .3206449{col 26}{space 2} .0614627{col 37}{space 1}    5.22{col 46}{space 3}0.000{col 54}{space 4} .2195478{col 67}{space 3} .4217421
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .32064492
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00377766
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+14.5
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+14.5
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+14.5
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
.    est restore m2a
{txt}(results {stata estimates replay m2a:m2a} are active now)

{com}.  margins, at(party_control=0) pwcompare level (90) //55%
{txt}{p 0 6 2}note: ignoring pwcompare options because there are no margins for making pairwise comparisons.{p_end}
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 13:party_control} = {res:{ralign 1:0}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5175568{col 26}{space 2} .0754592{col 37}{space 1}    6.86{col 46}{space 3}0.000{col 54}{space 4} .3934375{col 67}{space 3}  .641676
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .51755675
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00569408
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+15.5
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+15.5
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+15.5
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. su controls_total, d

                  {txt}Total number of controls
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              0
{txt} 5%    {res}        2              0
{txt}10%    {res}        3              1       {txt}Obs         {res}        393
{txt}25%    {res}        4              1       {txt}Sum of wgt. {res}        393

{txt}50%    {res}        5                      {txt}Mean          {res} 5.274809
                        {txt}Largest       Std. dev.     {res} 1.869683
{txt}75%    {res}        6              8
{txt}90%    {res}        8              8       {txt}Variance      {res} 3.495716
{txt}95%    {res}        8              8       {txt}Skewness      {res}-.4353152
{txt}99%    {res}        8              8       {txt}Kurtosis      {res} 2.744473
{txt}
{com}.  est restore m2a
{txt}(results {stata estimates replay m2a:m2a} are active now)

{com}.  margins, at((min)controls_total)  level (90) //60% für 0 (46% p25=4)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 14:controls_total} = {res:{ralign 1:0}} {txt:(min)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5914944{col 26}{space 2} .0947308{col 37}{space 1}    6.24{col 46}{space 3}0.000{col 54}{space 4} .4356761{col 67}{space 3} .7473128
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .59149443
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00897393
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+17
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+17
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+17
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
.  est restore m2a
{txt}(results {stata estimates replay m2a:m2a} are active now)

{com}.  margins, at((max)controls_total) level (90) //3% =6 (32 für max)
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 14:controls_total} = {res:{ralign 1:8}} {txt:(max)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .3165268{col 26}{space 2} .0641921{col 37}{space 1}    4.93{col 46}{space 3}0.000{col 54}{space 4} .2109403{col 67}{space 3} .4221134
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .31652685
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00412062
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'+18
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'+18
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'+18
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. su partyeffect

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        393    .3740458    .4844923          0          1
{txt}
{com}. 
. * twoway plot with addplot option
. twoway ||  scatter effect graph_x, yaxis(1) yscale(range(0 0.8)  axis(1))   ///
>                 title("Conceptualization and status of partisan effects",size(medsmall)) ///
>                 ytitle("Predicted probabilities", size(medsmall) axis(1))  ///
>             ylabel(0(0.2)0.8, angle(0) nogrid) msymbol(o) ///
>                 xscale(range(-0.25 18.25)) ///
>                 yline(0.37, lpattern(dash) lwidth(thin)) ///
>                 xlabel(0 "Min" 1 "Max"  2.5 "Gini" 3.5 "Ratio" 4.5 "Share" 6 "Pre" 7 "Pos" 8 "Pr/Po" 9.5 "No" 10.5 "Yes" 12 "No" 13 "Yes" 14.5 "Ctrl" 15.5 "IV" 17 "Min" 18 "Max", nogrid labsize(medsmall))     mcolor(black)  ///
>                  text(0.02 0.5 "Golden age", size(medsmall))  /// 
>                  text(0.02 7 "Type of Gini", size(medsmall))  /// 
>                  text(0.02 17.5 "Channels", size(medsmall))  /// 
>                  text(0.02 15 "Status", size(medsmall))  /// 
>                  text(0.02 12.5 "Cumulative", size(medsmall))  /// 
>                  text(0.02 10 "Top income", size(medsmall))  /// 
>                  text(0.02 3.5 "Type of inequality", size(medsmall))  /// 
>                  xline(1.75, lwidth(thin)) xline(5.25, lpattern(dot) lwidth(thin)) xline(8.75, lpattern(dot)lwidth(thin)) xline(11.25, lpattern(solid)lwidth(thin)) xline(16.25, lpattern(dot)lwidth(thin)) xline(13.75, lpattern(dot)lwidth(thin))  ///
>         || rcap upper lower graph_x, yaxis(1) ///
>                 lcolor(black) ysize(3) xsize(6) ///
>                 graphregion(color(white)) xtitle("") ///
>                 legend(off) title("",size(small)) ///
>  text(0.79 0.5 "I. Time")  text(0.79 7 "II. Inequality") text(0.79 15 "III. Partisanship")
{p 0 4 2}
{txt}(note:  named style
thin not found in class
linestyle,  default attributes used)
{p_end}
{res}{txt}
{com}.                 
. drop graph_id graph_x effect upper lower 
{txt}
{com}. 
. cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export "Fig_4.png", width(3600) replace           
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_4.png{rm}
saved as
PNG
format
{p_end}

{com}. graph export "Fig_4.tif", width(3600) replace                   
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_4.tif{rm}
saved as
TIFF
format
{p_end}

{com}. cd  "$DATADIR"  
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.  
. *Figure 5: Combined predicted probabilities for partisan effects on inequalty
. 
. *Full model with all variables and the number of relevant controls
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. melogit partyeffect  1.toprest  1.ideol_cum 1.party_control  c.jipf c.n_obs c.controls_total goldenshare  , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-210.06493}  
Iteration 1:{space 2}Log likelihood = {res:-209.47081}  
Iteration 2:{space 2}Log likelihood = {res:-209.46976}  
Iteration 3:{space 2}Log likelihood = {res:-209.46976}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-173.59674}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-173.59674}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-164.39458}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-161.12258}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-160.20778}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-160.12978}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-160.12905}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-160.12901}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-160.12901}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    18.46
{txt}Log pseudolikelihood = {res}-160.12901{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0101
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.toprest {c |}{col 14}{res}{space 2} 2.343037{col 26}{space 2} 1.081403{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .2235263{col 67}{space 3} 4.462547
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.215504{col 26}{space 2} 1.077868{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} 1.102922{col 67}{space 3} 5.328085
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.966817{col 26}{space 2} 1.073582{col 37}{space 1}   -1.83{col 46}{space 3}0.067{col 54}{space 4}-4.070999{col 67}{space 3} .1373662
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6569371{col 26}{space 2} .3522677{col 37}{space 1}   -1.86{col 46}{space 3}0.062{col 54}{space 4}-1.347369{col 67}{space 3} .0334948
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0044522{col 26}{space 2} .0024775{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4}-.0004037{col 67}{space 3} .0093081
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3404235{col 26}{space 2} .1654117{col 37}{space 1}   -2.06{col 46}{space 3}0.040{col 54}{space 4}-.6646245{col 67}{space 3}-.0162225
{txt}{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.261786{col 26}{space 2} 2.912911{col 37}{space 1}   -0.78{col 46}{space 3}0.437{col 54}{space 4}-7.970987{col 67}{space 3} 3.447416
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.3659238{col 26}{space 2}  1.43627{col 37}{space 1}   -0.25{col 46}{space 3}0.799{col 54}{space 4} -3.18096{col 67}{space 3} 2.449113
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.247118{col 26}{space 2} 4.606258{col 54}{space 4} 2.759822{col 67}{space 3} 24.64469
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store mx
{txt}
{com}. 
. *Prepare variables for graph
. gen graph_id=_n in 1/15
{txt}(378 missing values generated)

{com}. gen graph_x=graph_id-1
{txt}(378 missing values generated)

{com}. 
. gen effect=.
{txt}(393 missing values generated)

{com}. gen upper=.
{txt}(393 missing values generated)

{com}. gen lower=.
{txt}(393 missing values generated)

{com}. 
. *Variable for distribution of observations
. gen frequency=.
{txt}(393 missing values generated)

{com}. 
. gen factors=.
{txt}(393 missing values generated)

{com}. 
. *0 All at the lower values
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==1 &toprest==0 &ideol_cum==0 & controls_total>=6 , post level(90) //11%, n=29
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:29}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .1140855{col 26}{space 2} .0524673{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0277846{col 67}{space 3} .2003865
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .11408551
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00275281
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==0 &ideol_cum==0  & controls_total>=6 
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=0 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}29
{txt}(1 real change made)
(1 real change made)

{com}. 
. 
. **1 factor at party effect value
. *5  party IV 
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==0 &toprest==0 &ideol_cum==0 & controls_total>=6 , post level(90) //17%
{res}
{txt}{col 1}Predictive margins{col 62}{lalign 13:Number of obs}{col 75} = {res}{ralign 1:7}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .1773509{col 26}{space 2} .0793786{col 37}{space 1}    2.23{col 46}{space 3}0.025{col 54}{space 4} .0467848{col 67}{space 3} .3079171
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .17735092
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00630096
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==0 &toprest==0 &ideol_cum==0  & controls_total<=5 //7
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=1 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}31
{txt}(1 real change made)
(1 real change made)

{com}. 
. **Toprest
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==1 &toprest==1 &ideol_cum==0 & controls_total>=6 , post level(90) //26%
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:46}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .2622098{col 26}{space 2} .0751299{col 37}{space 1}    3.49{col 46}{space 3}0.000{col 54}{space 4} .1386322{col 67}{space 3} .3857874
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
       _cons
r1 {res} .2622098
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
          _cons
_cons {res} .0056445
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==1 &ideol_cum==1  & controls_total>=6 //38
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=1 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}7
{txt}(1 real change made)
(1 real change made)

{com}. 
. *6 Cumulative
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==1 &toprest==0 &ideol_cum==1 & controls_total>=6 , post level(90) //31%
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:62}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .3123715{col 26}{space 2} .0927024{col 37}{space 1}    3.37{col 46}{space 3}0.001{col 54}{space 4} .1598896{col 67}{space 3} .4648533
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .31237145
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00859373
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+2
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==1 &ideol_cum==1  & controls_total>=6 //62
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=1 if graph_x==`j'
{txt}  9{com}. 
. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}7
{txt}(1 real change made)
(1 real change made)

{com}. 
. ***2 factors at party effect value - all good
. 
. *5  party IV & channels
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==0 &toprest==0 &ideol_cum==0 & controls_total<=5 , post level(90) //27%
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:31}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .2733561{col 26}{space 2} .0898746{col 37}{space 1}    3.04{col 46}{space 3}0.002{col 54}{space 4} .1255256{col 67}{space 3} .4211866
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
       _cons
r1 {res} .2733561
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00807744
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+3
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==0 &toprest==0 &ideol_cum==0  & controls_total<=5 //22
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=2 if graph_x==`j'
{txt}  9{com}. 
. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}31
{txt}(1 real change made)
(1 real change made)

{com}. 
. **Party IV & Toprest
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==0 &toprest==1 &ideol_cum==0 & controls_total>=6 , post level(90) //38%
{res}
{txt}{col 1}Predictive margins{col 62}{lalign 13:Number of obs}{col 75} = {res}{ralign 1:9}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .3818545{col 26}{space 2} .1077124{col 37}{space 1}    3.55{col 46}{space 3}0.000{col 54}{space 4} .2046834{col 67}{space 3} .5590256
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
       _cons
r1 {res} .3818545
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .01160195
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+4
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==1 &ideol_cum==1  & controls_total>=6 //9
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=2 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}7
{txt}(1 real change made)
(1 real change made)

{com}. 
. 
. *7 Low channels & Toprest
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==1 &toprest==1 &ideol_cum==0 & controls_total<=5 , post level(90) //44%
{res}
{txt}{col 1}Predictive margins{col 62}{lalign 13:Number of obs}{col 75} = {res}{ralign 1:7}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4392648{col 26}{space 2} .1296922{col 37}{space 1}    3.39{col 46}{space 3}0.001{col 54}{space 4} .2259401{col 67}{space 3} .6525894
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .43926479
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .01682006
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+5
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==1 &ideol_cum==0  & controls_total<=5  //7
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=2 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}7
{txt}(1 real change made)
(1 real change made)

{com}. 
. *6 Cumulative & channels
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==1 &toprest==0 &ideol_cum==1 & controls_total<=5 , post level(90) //42%
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:15}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4157411{col 26}{space 2} .0937727{col 37}{space 1}    4.43{col 46}{space 3}0.000{col 54}{space 4} .2614987{col 67}{space 3} .5699835
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .41574111
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00879332
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+6
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==1 &ideol_cum==1  & controls_total>=6 //15
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=2 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}7
{txt}(1 real change made)
(1 real change made)

{com}. 
. *6 Cumulative measure & toprest
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==1 &toprest==1 &ideol_cum==1 & controls_total>=6 , post level(90) //43%
{res}
{txt}{col 1}Predictive margins{col 62}{lalign 13:Number of obs}{col 75} = {res}{ralign 1:7}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4344743{col 26}{space 2} .1740202{col 37}{space 1}    2.50{col 46}{space 3}0.013{col 54}{space 4} .1482364{col 67}{space 3} .7207121
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .43447427
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .03028305
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+7
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==1 &ideol_cum==1  & controls_total>=6 //7
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.                 replace factors=2 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}7
{txt}(1 real change made)
(1 real change made)

{com}. 
. *Party IV & cumulative
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==0 &toprest==0 &ideol_cum==1 & controls_total>=6 , post level(90) //45%
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:13}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .4523642{col 26}{space 2} .1269313{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .2435807{col 67}{space 3} .6611476
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .45236418
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .01611156
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+8
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==1 &ideol_cum==1  & controls_total>=6 //13
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.                 replace factors=2 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}7
{txt}(1 real change made)
(1 real change made)

{com}. 
. *1 Low, 3 high
. *5 Only party Control 
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==1 &toprest==1 &ideol_cum==1 & controls_total<=5 , post level(90) //66%
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:33}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .6626748{col 26}{space 2} .1066107{col 37}{space 1}    6.22{col 46}{space 3}0.000{col 54}{space 4} .4873158{col 67}{space 3} .8380338
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .66267476
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .01136584
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+9
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==0 &toprest==0 &ideol_cum==0  & controls_total<=5 //33
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=3 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}31
{txt}(1 real change made)
(1 real change made)

{com}. 
. **Only toprest=0
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==0 &toprest==0 &ideol_cum==1 & controls_total<=5 , post level(90) //57%
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:34}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5712276{col 26}{space 2} .0935661{col 37}{space 1}    6.11{col 46}{space 3}0.000{col 54}{space 4} .4173251{col 67}{space 3} .7251301
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .57122758
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00875461
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+10
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==1 &ideol_cum==1  & controls_total>=6 //34
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'  
{txt}  8{com}.         replace factors=3 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}7
{txt}(1 real change made)
(1 real change made)

{com}. 
. *6 Only short term
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==0 &toprest==1 &ideol_cum==0 & controls_total<=5 , post level(90) //55%
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:48}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .5459062{col 26}{space 2} .0828533{col 37}{space 1}    6.59{col 46}{space 3}0.000{col 54}{space 4} .4096246{col 67}{space 3} .6821878
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
       _cons
r1 {res} .5459062
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00686468
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+11
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==1 &ideol_cum==1  & controls_total<=5 //42
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=3 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}33
{txt}(1 real change made)
(1 real change made)

{com}. 
. *6 Only channels=0
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==0 &toprest==1 &ideol_cum==1 & controls_total>=6 , post level(90) //72%
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:19}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .7223229{col 26}{space 2} .1073052{col 37}{space 1}    6.73{col 46}{space 3}0.000{col 54}{space 4} .5458215{col 67}{space 3} .8988243
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .72232288
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .01151441
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+12
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==1 &toprest==1 &ideol_cum==1  & controls_total>=6 //19
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=3 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}7
{txt}(1 real change made)
(1 real change made)

{com}. 
. *13 All high
. est restore mx
{txt}(results {stata estimates replay mx:mx} are active now)

{com}. margins if party_control==0 &toprest==1 &ideol_cum==1 & controls_total<=5 , post level(90) //78%
{res}
{txt}{col 1}Predictive margins{col 61}{lalign 13:Number of obs}{col 74} = {res}{ralign 2:12}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .7752883{col 26}{space 2} .0929762{col 37}{space 1}    8.34{col 46}{space 3}0.000{col 54}{space 4} .6223561{col 67}{space 3} .9282206
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        _cons
r1 {res} .77528835
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           _cons
_cons {res} .00864457
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+13
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}.         count if party_control==0 &toprest==1 &ideol_cum==1  & controls_total<=5  //12
{txt}  7{com}.         replace frequency=r(N) if graph_x==`j'
{txt}  8{com}.         replace factors=4 if graph_x==`j'
{txt}  9{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
  {res}12
{txt}(1 real change made)
(1 real change made)

{com}. 
. ***Combine predicted probabilities
. foreach var of varlist effect upper lower {c -(}
{txt}  2{com}. gen `var'_mean=.
{txt}  3{com}. {c )-}
{txt}(393 missing values generated)
(393 missing values generated)
(393 missing values generated)

{com}. 
. foreach var of varlist effect upper lower {c -(}
{txt}  2{com}. forvalues i=0(1)4 {c -(}
{txt}  3{com}.         su `var' if factors==`i'
{txt}  4{com}.         replace `var'_mean=r(mean)  if factors==`i'
{txt}  5{com}.         {c )-}
{txt}  6{com}. {c )-}

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}effect {c |}{res}          1    .1140855           .   .1140855   .1140855
{txt}(1 real change made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}effect {c |}{res}          3    .2506441    .0682493   .1773509   .3123715
{txt}(3 real changes made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}effect {c |}{res}          6    .3995092    .0664627   .2733561   .4523642
{txt}(6 real changes made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}effect {c |}{res}          4    .6255329    .0817258   .5459062   .7223229
{txt}(4 real changes made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}effect {c |}{res}          1    .7752883           .   .7752883   .7752883
{txt}(1 real change made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}upper {c |}{res}          1    .2003942           .   .2003942   .2003942
{txt}(1 real change made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}upper {c |}{res}          3     .386198    .0784698   .3079287   .4648668
{txt}(3 real changes made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}upper {c |}{res}          6    .5974584    .1054467   .4211998   .7207376
{txt}(6 real changes made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}upper {c |}{res}          4    .7860583    .0998665      .6822     .89884
{txt}(4 real changes made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}upper {c |}{res}          1    .9282342           .   .9282342   .9282342
{txt}(1 real change made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}lower {c |}{res}          1    .0277769           .   .0277769   .0277769
{txt}(1 real change made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}lower {c |}{res}          3    .1150901    .0601111   .0467732   .1598761
{txt}(3 real changes made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}lower {c |}{res}          6    .2015599    .0540142   .1255124    .261485
{txt}(6 real changes made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}lower {c |}{res}          4    .4650075    .0642102   .4096124   .5458058
{txt}(4 real changes made)

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}lower {c |}{res}          1    .6223425           .   .6223425   .6223425
{txt}(1 real change made)

{com}. 
. bysort factors: egen freq_tot=total(frequency)
{txt}
{com}. bysort factors: egen freq_mean=mean(frequency)
{txt}(378 missing values generated)

{com}. su freq_tot freq_mean

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}freq_tot {c |}{res}        393    2.249364    11.90348          0         78
{txt}{space 3}freq_mean {c |}{res}         15    15.33333    5.212165         11         29
{txt}
{com}. 
. twoway || bar freq_mean factors ,yaxis(2) ylab(0(10)30, axis(2)) yscale(range(0 10) axis(2)) fcolor(gs15) bcolor(gs15) barwidth(0.4)  ytitle("Average Number of observations", size(medsmall) axis(2)) ///
> ||  scatter effect_mean factors if factors<=4, yaxis(1) yscale(range(0 1)  axis(1))   ///
>                 title("Combined effects",size(medsmall)) ///
>                 ytitle("Predicted probabilities", size(medsmall) axis(1))  ///
>             ylabel(0(0.2)1, angle(0) nogrid) ///
>                 xscale(range(-0.5 4.5)) ///
>                 xlabel(0 1 2 3 4, nogrid  labsize(medsmall)) ///
>                 mcolor(black) msymbol(o) ///
>         || rcap upper_mean lower_mean factors if factors<=4, yaxis(1) ///
>                 lcolor(black) ysize(3) xsize(4) ///
>                 graphregion(color(white)) xtitle("") ///
>                 legend(off) title("")    xtitle("") ///
>                 || function y = 0.425,           ///
>                 range(-0.5 4.5) clpattern(dash) clcolor(black) clwidth(thin)  /// 
>                  name(gr1, replace) yscale(alt) yscale(alt axis(2))     ///
>                           xtitle(Number of predictors at values associated with partisan effects,size(medsmall)) //     
{res}{txt}
{com}.  
. cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export "Fig_5.png", width(3600) replace           
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_5.png{rm}
saved as
PNG
format
{p_end}

{com}. graph export "Fig_5.tif", width(3600) replace                           
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_5.tif{rm}
saved as
TIFF
format
{p_end}

{com}. cd  "$DATADIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. drop frequency graph_id graph_x upper lower effect
{txt}
{com}.  
. *****************************************************
. ************ SUPPLEMENTARY FILES ********************
. *****************************************************
. 
. 
. *****************************************************   
. ******** S-A: Descriptive information ******** 
. *****************************************************
. 
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. * Table S-A1: Descriptive information
. estpost tabstat partyeffect goldenshare ineq_meas gini_gr toprest jipf n_obs policies_control corp_control  postind_control glob_control controls_total, c(stat) stat(mean median sd min max n)

{txt}Summary statistics: mean p50 sd min max count
     for variables: partyeffect goldenshare ineq_meas gini_gr toprest jipf n_obs policies_control corp_control postind_control glob_control controls_total

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(p50)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}{space 1}{ralign 9:e(max)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:partyeffect}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3740458}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .4844923}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:goldenshare}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1793838}}}{space 1}{space 1}{ralign 9:{res:{sf: .2272727}}}{space 1}{space 1}{ralign 9:{res:{sf: .1444232}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .5757576}}}{space 1}
{space 0}{space 0}{ralign 12:ineq_meas}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 1.763359}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf: .8159638}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        3}}}{space 1}
{space 0}{space 0}{ralign 12:gini_gr}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 1.808511}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf: .6825024}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        3}}}{space 1}
{space 0}{space 0}{ralign 12:toprest}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4605598}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .4990774}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}
{space 0}{space 0}{ralign 12:jipf}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 1.710781}}}{space 1}{space 1}{ralign 9:{res:{sf:    1.545}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.193143}}}{space 1}{space 1}{ralign 9:{res:{sf:     .208}}}{space 1}{space 1}{ralign 9:{res:{sf:      6.8}}}{space 1}
{space 0}{space 0}{ralign 12:n_obs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  271.084}}}{space 1}{space 1}{ralign 9:{res:{sf:      239}}}{space 1}{space 1}{ralign 9:{res:{sf: 187.0731}}}{space 1}{space 1}{ralign 9:{res:{sf:       28}}}{space 1}{space 1}{ralign 9:{res:{sf:      694}}}{space 1}
{space 0}{space 0}{ralign 12:policies_c~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .7811705}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .9678251}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}
{space 0}{space 0}{ralign 12:corp_control}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 1.437659}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.016098}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        4}}}{space 1}
{space 0}{space 0}{ralign 12:postind_co~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 2.109415}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.165288}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        4}}}{space 1}
{space 0}{space 0}{ralign 12:glob_control}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .9465649}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .6465723}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}
{space 0}{space 0}{ralign 12:controls_t~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 5.274809}}}{space 1}{space 1}{ralign 9:{res:{sf:        5}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.869683}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        8}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}
{space 0}{space 0}{ralign 12:partyeffect}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}
{space 0}{space 0}{ralign 12:goldenshare}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}
{space 0}{space 0}{ralign 12:ineq_meas}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}
{space 0}{space 0}{ralign 12:gini_gr}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      188}}}{space 1}
{space 0}{space 0}{ralign 12:toprest}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}
{space 0}{space 0}{ralign 12:jipf}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}
{space 0}{space 0}{ralign 12:n_obs}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}
{space 0}{space 0}{ralign 12:policies_c~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}
{space 0}{space 0}{ralign 12:corp_control}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}
{space 0}{space 0}{ralign 12:postind_co~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}
{space 0}{space 0}{ralign 12:glob_control}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}
{space 0}{space 0}{ralign 12:controls_t~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      393}}}{space 1}

{com}. 
. cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab using Table_SA1.rtf, replace ///
>  cells("count ( fmt(%9.0f)) mean ( fmt(%9.2f)) sd ( fmt(%9.2f)) min ( fmt(%9.2f)) max( fmt(%9.2f)) ") noobs nonumber ///
>    collabels("Obs." "Mean" "Std. Dev" "Min" "Max") ///
>   nomtitle nonote label //
{res}{txt}(output written to {browse  `"Table_SA1.rtf"'})

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *Figure S-A1: Different policy channels and partisan effects on inequality
. 
. *Fig A1a: Corporatism
. 
. tab corp_control

{txt}Corporatism {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         83       21.12       21.12
{txt}          1 {c |}{res}        119       30.28       51.40
{txt}          2 {c |}{res}        134       34.10       85.50
{txt}          3 {c |}{res}         50       12.72       98.22
{txt}          4 {c |}{res}          7        1.78      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        393      100.00
{txt}
{com}. xtile corp_control_2=corp_control, nq(2)
{txt}
{com}. bysort corp_control_2: su partyeffect

{txt}{hline}
-> corp_control_2 = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        202     .539604    .4996674          0          1

{txt}{hline}
-> corp_control_2 = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        191    .1989529    .4002617          0          1

{txt}
{com}. bysort corp_control_2: tab corp_control

{txt}{hline}
-> corp_control_2 = 1

Corporatism {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         83       41.09       41.09
{txt}          1 {c |}{res}        119       58.91      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        202      100.00

{txt}{hline}
-> corp_control_2 = 2

Corporatism {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}        134       70.16       70.16
{txt}          3 {c |}{res}         50       26.18       96.34
{txt}          4 {c |}{res}          7        3.66      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        191      100.00

{txt}
{com}. ttest partyeffect, by(corp_control_2) // 0-1 control: 55%, 2-4 Controls: 20%

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}    202{col 22}  .539604{col 34} .0351565{col 46} .4996674{col 58} .4702811{col 70} .6089268
       {txt}2 {c |}{res}{col 12}    191{col 22} .1989529{col 34} .0289619{col 46} .4002617{col 58} .1418247{col 70} .2560811
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    393{col 22} .3740458{col 34} .0244394{col 46} .4844923{col 58} .3259971{col 70} .4220945
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .3406511{col 34} .0458295{col 58} .2505481{col 70} .4307541
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res}  7.4330
{txt}H0: diff = 0                                     Degrees of freedom = {res}     391

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect (count) n=partyeffect  , by(corp_control_2)
{res}{txt}
{com}. 
. * upper and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}.         
. generate graphvar = 3   if corp_control_2 == 1
{txt}(1 missing value generated)

{com}. replace  graphvar = 5  if corp_control_2 == 2
{txt}(1 real change made)

{com}. 
. 
. twoway (bar meanvar graphvar, lcolor(black) graphregion(fcolor(white) ) bargap(200)  fintensity(0) plotregion(color(white) ) ///
>         yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid)  ///
>         xlabel(3 "0-1 variables" 5 ">1 variables", valuelabel angle(horizontal) nogrid) ytitle("% Direct party effect") ///
>         title("a)", size(medium)) xtitle("Corporatism", size(medsmall))legend(off) name(gr4a, replace) ) ///
>          (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black) ) ///
>          (rcap hivar lowar graphvar, alcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid))
{res}{txt}
{com}. 
. *Fig.A1b) policies_control
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. bysort policies_control: su partyeffect

{txt}{hline}
-> policies_control = 0

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        175    .2685714    .4444882          0          1

{txt}{hline}
-> policies_control = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        160       .5625    .4976359          0          1

{txt}{hline}
-> policies_control = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         47    .1276596    .3373181          0          1

{txt}{hline}
-> policies_control = 3

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}          1           1           .          1          1

{txt}{hline}
-> policies_control = 5

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         10          .3    .4830459          0          1

{txt}
{com}. xtile policies_control_2=policies_control, nq(2)
{txt}
{com}. bysort policies_control_2: su policies_control 

{txt}{hline}
-> policies_control_2 = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
policies_c~l {c |}{res}        335    .4776119    .5002457          0          1

{txt}{hline}
-> policies_control_2 = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
policies_c~l {c |}{res}         58    2.534483    1.142719          2          5

{txt}
{com}. ttest partyeffect, by(policies_control_2) //30% vs. 55%

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}    335{col 22} .4089552{col 34} .0269014{col 46} .4923764{col 58} .3560377{col 70} .4618727
       {txt}2 {c |}{res}{col 12}     58{col 22} .1724138{col 34} .0500328{col 46} .3810388{col 58} .0722247{col 70} .2726028
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    393{col 22} .3740458{col 34} .0244394{col 46} .4844923{col 58} .3259971{col 70} .4220945
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .2365414{col 34} .0679475{col 58} .1029534{col 70} .3701295
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res}  3.4812
{txt}H0: diff = 0                                     Degrees of freedom = {res}     391

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9997         {txt}Pr(|T| > |t|) = {res}0.0006          {txt}Pr(T > t) = {res}0.0003
{txt}
{com}. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect (count) n=partyeffect  , by(policies_control_2)
{res}{txt}
{com}. 
. * upper and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}.         
. generate graphvar = 5   if policies_control_2 == 2
{txt}(1 missing value generated)

{com}. replace  graphvar = 3  if policies_control_2 == 1
{txt}(1 real change made)

{com}. 
. twoway (bar meanvar graphvar, lcolor(black) graphregion(fcolor(white) ) bargap(200)  fintensity(0) plotregion(color(white) ) ///
>         yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid)  ///
>         xlabel(3 "0-1 variables" 5 ">1 variables", valuelabel angle(horizontal) nogrid)  ytitle("% Direct party effect") ///
>         title("b)", size(medium)) xtitle("Policies", size(medsmall))legend(off) name(gr4b, replace) ) ///
>          (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black) )  ///
>          (rcap hivar lowar graphvar, lcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid))
{res}{txt}
{com}.                  
. *Fig.A1c) Globalization
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. xtile glob_control_2=glob_control, nq(2)
{txt}
{com}. 
. bysort glob_control_2: su partyeffect

{txt}{hline}
-> glob_control_2 = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        321    .3894081    .4883774          0          1

{txt}{hline}
-> glob_control_2 = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}         72    .3055556    .4638749          0          1

{txt}
{com}. bysort glob_control_2: su glob_control

{txt}{hline}
-> glob_control_2 = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
glob_control {c |}{res}        321    .7102804    .4543404          0          1

{txt}{hline}
-> glob_control_2 = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
glob_control {c |}{res}         72           2           0          2          2

{txt}
{com}. replace glob_control_2=2 if glob_control==1
{txt}(228 real changes made)

{com}. ttest partyeffect, by(glob_control_2) //0.39 vs. 0.4%

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}     93{col 22} .4516129{col 34} .0518839{col 46} .5003505{col 58} .3485669{col 70} .5546589
       {txt}2 {c |}{res}{col 12}    300{col 22}      .35{col 34} .0275839{col 46} .4777665{col 58} .2957169{col 70} .4042831
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    393{col 22} .3740458{col 34} .0244394{col 46} .4844923{col 58} .3259971{col 70} .4220945
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1016129{col 34} .0573455{col 58}-.0111311{col 70} .2143569
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res}  1.7719
{txt}H0: diff = 0                                     Degrees of freedom = {res}     391

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9614         {txt}Pr(|T| > |t|) = {res}0.0772          {txt}Pr(T > t) = {res}0.0386
{txt}
{com}. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect (count) n=partyeffect  , by(glob_control_2)
{res}{txt}
{com}. 
. * upper and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}.         
. generate graphvar = 3   if glob_control_2 == 1
{txt}(1 missing value generated)

{com}. replace  graphvar = 5  if glob_control_2 == 2
{txt}(1 real change made)

{com}. 
. twoway (bar meanvar graphvar, lcolor(black) graphregion(fcolor(white) ) bargap(200)  fintensity(0) plotregion(color(white) ) ///
>         yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid)  ///
>         xlabel(3 "0-1 variables" 5 ">1 variables", valuelabel angle(horizontal) nogrid) ytitle("% Direct party effect") ///
>         title("c)", size(medium)) xtitle("Globalization", size(medsmall))legend(off) name(gr4c, replace) ) ///
>          (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black) ) ///
>          (rcap hivar lowar graphvar, alcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid))
{res}{txt}
{com}.           
. *Fig.A1d) Postindustrialization
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. xtile postind_control_2=postind_control, nq(2)
{txt}
{com}. 
. 
. bysort postind_control_2: su partyeffect

{txt}{hline}
-> postind_control_2 = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        222    .3738739    .4849242          0          1

{txt}{hline}
-> postind_control_2 = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}partyeffect {c |}{res}        171     .374269    .4853548          0          1

{txt}
{com}. bysort postind_control_2: su glob_control

{txt}{hline}
-> postind_control_2 = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
glob_control {c |}{res}        222    .7927928    .7070059          0          2

{txt}{hline}
-> postind_control_2 = 2

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
glob_control {c |}{res}        171    1.146199    .4931593          0          2

{txt}
{com}. ttest partyeffect, by(postind_control_2) //0.35 vs. 0.46%

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}    222{col 22} .3738739{col 34}  .032546{col 46} .4849242{col 58} .3097337{col 70} .4380141
       {txt}2 {c |}{res}{col 12}    171{col 22}  .374269{col 34}  .037116{col 46} .4853548{col 58} .3010014{col 70} .4475366
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    393{col 22} .3740458{col 34} .0244394{col 46} .4844923{col 58} .3259971{col 70} .4220945
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0003951{col 34} .0493586{col 58}-.0974367{col 70} .0966464
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res} -0.0080
{txt}H0: diff = 0                                     Degrees of freedom = {res}     391

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.4968         {txt}Pr(|T| > |t|) = {res}0.9936          {txt}Pr(T > t) = {res}0.5032
{txt}
{com}. collapse (mean) meanvar= partyeffect (sd) sdwrite=partyeffect (count) n=partyeffect  , by(postind_control_2)
{res}{txt}
{com}. 
. * upper and lower values of the confidence interval.
.     generate hivar = meanvar + invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.     generate lowar = meanvar - invttail(n-1,0.05)*(sdwrite / sqrt(n))
{txt}
{com}.         replace lowar = 0 if lowar<0
{txt}(0 real changes made)

{com}.         
. generate graphvar = 3   if postind_control_2 == 1
{txt}(1 missing value generated)

{com}. replace  graphvar = 5  if postind_control_2 == 2
{txt}(1 real change made)

{com}. 
. 
. twoway (bar meanvar graphvar, lcolor(black) graphregion(fcolor(white) ) bargap(200)  fintensity(0) plotregion(color(white) ) ///
>         yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid)  ///
>         xlabel(3 "0-1 variables" 5 ">1 variables", valuelabel angle(horizontal) nogrid) ytitle("% Direct party effect") ///
>         title("d)", size(medium)) xtitle("Postindustrialization", size(medsmall))legend(off) name(gr4d, replace) ) ///
>          (scatter meanvar graphvar, ms(i) mlab(n) mlabpos(2) mlabcolor(black) ) ///
>          (rcap hivar lowar graphvar, alcolor(black) yscale(range(0(0.2)0.8)) ylabel(0(0.2)0.8, nogrid))
{res}{txt}
{com}.          
. graph combine gr4a gr4b gr4c gr4d, ycommon xcommon row(2) ysize(4) xsize(5) ///
>         title("Policy channels", size(medium)) //
{res}{txt}
{com}. cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export Fig_SA1.png, replace width(3600)
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_SA1.png{rm}
saved as
PNG
format
{p_end}

{com}. cd  "$DATADIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. 
. *****************************************************   
. *********** S-B: Examining publication bias *********
. *****************************************************
. 
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. *Table S-B1: Explaining partisan effects on inequality: Journal impact and policy channelsest clear
. 
. melogit partyeffect 1.party_control##c.jipf c.controls_total c.n_obs , vce(robust)  ||paper_id: 
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-232.77579}  
Iteration 1:{space 2}Log likelihood = {res:-231.47503}  
Iteration 2:{space 2}Log likelihood = {res:-231.46437}  
Iteration 3:{space 2}Log likelihood = {res:-231.46436}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-183.84181}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-183.84181}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-174.06243}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-171.49154}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-171.04625}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-171.02965}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-171.03001}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-171.03003}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-171.03004}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}5{txt}){col 67}={res}{col 70}    16.97
{txt}Log pseudolikelihood = {res}-171.03004{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0046
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2} 2.008259{col 26}{space 2}  1.74791{col 37}{space 1}    1.15{col 46}{space 3}0.251{col 54}{space 4}-1.417583{col 67}{space 3}   5.4341
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} .3301695{col 26}{space 2} .5753365{col 37}{space 1}    0.57{col 46}{space 3}0.566{col 54}{space 4}-.7974693{col 67}{space 3} 1.457808
{txt}{space 12} {c |}
party_cont~l#{c |}
{space 6}c.jipf {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.627463{col 26}{space 2} .7902662{col 37}{space 1}   -2.06{col 46}{space 3}0.039{col 54}{space 4}-3.176356{col 67}{space 3}-.0785696
{txt}{space 12} {c |}
controls_t~l {c |}{col 14}{res}{space 2}-.3504249{col 26}{space 2} .1559662{col 37}{space 1}   -2.25{col 46}{space 3}0.025{col 54}{space 4}-.6561131{col 67}{space 3}-.0447367
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0015934{col 26}{space 2} .0024141{col 37}{space 1}    0.66{col 46}{space 3}0.509{col 54}{space 4}-.0031383{col 67}{space 3}  .006325
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0875192{col 26}{space 2} 1.302757{col 37}{space 1}    0.07{col 46}{space 3}0.946{col 54}{space 4}-2.465837{col 67}{space 3} 2.640876
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 7.457852{col 26}{space 2} 3.610695{col 54}{space 4} 2.887402{col 67}{space 3} 19.26284
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m4
{txt}
{com}. melogit partyeffect 1.party_control##c.jipf##c.controls_total c.n_obs  , vce(robust)  ||paper_id: 
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-226.54453}  
Iteration 1:{space 2}Log likelihood = {res:-225.45385}  
Iteration 2:{space 2}Log likelihood = {res:-225.44135}  
Iteration 3:{space 2}Log likelihood = {res:-225.44132}  
Iteration 4:{space 2}Log likelihood = {res:-225.44132}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-181.79717}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-181.79717}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-171.41439}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-168.12474}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-167.88717}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-167.88201}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-167.88195}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-167.88195}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    23.19
{txt}Log pseudolikelihood = {res}-167.88195{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0031
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2} 3.075564{col 26}{space 2} 3.330551{col 37}{space 1}    0.92{col 46}{space 3}0.356{col 54}{space 4}-3.452195{col 67}{space 3} 9.603323
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-1.325439{col 26}{space 2} .6907195{col 37}{space 1}   -1.92{col 46}{space 3}0.055{col 54}{space 4}-2.679225{col 67}{space 3} .0283461
{txt}{space 12} {c |}
party_cont~l#{c |}
{space 6}c.jipf {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.195559{col 26}{space 2} 1.248563{col 37}{space 1}   -0.96{col 46}{space 3}0.338{col 54}{space 4}-3.642697{col 67}{space 3} 1.251579
{txt}{space 12} {c |}
controls_t~l {c |}{col 14}{res}{space 2}-1.194165{col 26}{space 2} .5181635{col 37}{space 1}   -2.30{col 46}{space 3}0.021{col 54}{space 4}-2.209747{col 67}{space 3}-.1785831
{txt}{space 12} {c |}
party_cont~l#{c |}
{space 10}c. {c |}
controls_t~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2} .2908418{col 26}{space 2} .7027148{col 37}{space 1}    0.41{col 46}{space 3}0.679{col 54}{space 4}-1.086454{col 67}{space 3} 1.668137
{txt}{space 12} {c |}
{space 6}c.jipf#{c |}
{space 10}c. {c |}
controls_t~l {c |}{col 14}{res}{space 2} .6100905{col 26}{space 2} .2751944{col 37}{space 1}    2.22{col 46}{space 3}0.027{col 54}{space 4} .0707193{col 67}{space 3} 1.149462
{txt}{space 12} {c |}
party_cont~l#{c |}
{space 6}c.jipf#{c |}
{space 10}c. {c |}
controls_t~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.3906391{col 26}{space 2} .3329656{col 37}{space 1}   -1.17{col 46}{space 3}0.241{col 54}{space 4} -1.04324{col 67}{space 3} .2619615
{txt}{space 12} {c |}
{space 7}n_obs {c |}{col 14}{res}{space 2} .0023177{col 26}{space 2} .0026161{col 37}{space 1}    0.89{col 46}{space 3}0.376{col 54}{space 4}-.0028097{col 67}{space 3} .0074452
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.052103{col 26}{space 2} 1.586282{col 37}{space 1}    1.29{col 46}{space 3}0.196{col 54}{space 4}-1.056953{col 67}{space 3} 5.161158
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 6.415401{col 26}{space 2} 3.043882{col 54}{space 4} 2.531388{col 67}{space 3} 16.25882
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m5
{txt}
{com}. 
. *Output:  
.  cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab  m4 m5 using Table_B1.rtf, label replace compress wide onecell nogaps star(* 0.10 ** 0.05 *** 0.01) ///
> se(2) b(2) aic(2) bic(2) mtitle("Model 1" "Model 2") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff." ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
> controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *party_control *jipf *controls_total)
{res}{txt}(output written to {browse  `"Table_B1.rtf"'})

{com}. cd  "$DATADIR"  
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.  
. *Figure S-B1: Predicted probability of partisan effect conditional on variable status and impact 
.  
. *Two-way interaction
. est restore m4
{txt}(results {stata estimates replay m4:m4} are active now)

{com}. margins, over(party_control) at((p25) jipf) at((p75) jipf)  level(90) post
{res}
{txt}{col 1}Predictive margins{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:Over:}{res:party_control}{p_end}
{p2colreset}{...}
1._at: {space 0}{res:0.party_control}
{lalign 7:}{space 4}{lalign 4:jipf} = {res:{ralign 6:.904}} {txt:(p25)}
{space 7}{res:1.party_control}
{lalign 7:}{space 4}{lalign 4:jipf} = {res:{ralign 6:1.028}} {txt:(p25)}
2._at: {space 0}{res:0.party_control}
{lalign 7:}{space 4}{lalign 4:jipf} = {res:{ralign 6:2.079}} {txt:(p75)}
{space 7}{res:1.party_control}
{lalign 7:}{space 4}{lalign 4:jipf} = {res:{ralign 6:2.1345}} {txt:(p75)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at#{c |}
party_cont~l {c |}
{space 7}1#no  {c |}{col 14}{res}{space 2} .4005671{col 26}{space 2}  .085146{col 37}{space 1}    4.70{col 46}{space 3}0.000{col 54}{space 4} .2605145{col 67}{space 3} .5406197
{txt}{space 6}1#yes  {c |}{col 14}{res}{space 2} .3973828{col 26}{space 2} .0955338{col 37}{space 1}    4.16{col 46}{space 3}0.000{col 54}{space 4} .2402436{col 67}{space 3}  .554522
{txt}{space 7}2#no  {c |}{col 14}{res}{space 2} .4469391{col 26}{space 2} .0784359{col 37}{space 1}    5.70{col 46}{space 3}0.000{col 54}{space 4} .3179235{col 67}{space 3} .5759547
{txt}{space 6}2#yes  {c |}{col 14}{res}{space 2} .2475924{col 26}{space 2} .0479653{col 37}{space 1}    5.16{col 46}{space 3}0.000{col 54}{space 4} .1686965{col 67}{space 3} .3264883
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}b[1,4]
           1._at#        1._at#        2._at#        2._at#
    0.party_co~l  1.party_co~l  0.party_co~l  1.party_co~l
r1 {res}    .40056709     .39738283     .44693909     .24759241
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[4,4]
                     1._at#        1._at#        2._at#        2._at#
              0.party_co~l  1.party_co~l  0.party_co~l  1.party_co~l
       1._at#
0.party_co~l {res}    .00724983
{txt}       1._at#
1.party_co~l {res}    .00022893     .00912671
{txt}       2._at#
0.party_co~l {res}      .003622    -5.443e-06     .00615219
{txt}       2._at#
1.party_co~l {res}   -.00035777     .00333384     .00024167     .00230067
{reset}
{com}. 
. gen graph_id=_n in 1/4
{txt}(389 missing values generated)

{com}. gen graph_x=graph_id-1
{txt}(389 missing values generated)

{com}. 
. gen effect=.
{txt}(393 missing values generated)

{com}. gen upper=.
{txt}(393 missing values generated)

{com}. gen lower=.
{txt}(393 missing values generated)

{com}. 
. forval i=1/4 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.96*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.96*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. twoway ||  scatter effect graph_x, yaxis(1) yscale(range(0 0.8)  axis(1))   ///
>                 title("",size(medsmall)) ///            xtitle("Journal impact factor",size(medsmall)) ///
>                 ytitle("Predicted probabilities", size(medsmall) axis(1)) msymbol(o)  ///
>             ylabel(0(0.1)0.8, angle(0) nogrid) ///
>                 xscale(range(-0.5 3.5)) ///
>                 xlabel(0  "IV/JIF(p25)" 1 "Ctrl/JIF(p25)" 2 "IV/JIF(p75)" 3 "Ctrl/JIF(p75)", nogrid) ///
>                 mcolor(black) ///
>         || rcap upper lower graph_x, yaxis(1) ///
>                 lcolor(black) ysize(4) xsize(5) ///
>                 graphregion(color(white)) ///
>                 legend(off) xtitle("") ///
>                 yline(0.375, lpattern(dash)) name(gr1, replace) //
{p 0 4 2}
{txt}(note:  named style
thin not found in class
linestyle,  default attributes used)
{p_end}
{res}{txt}
{com}. 
.  cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export "Fig_SB1.png", width(3600) replace                 
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_SB1.png{rm}
saved as
PNG
format
{p_end}

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.         
. drop graph_id graph_x effect upper lower
{txt}
{com}. 
. *Figure S-B2: Marginal effect of party status conditional on Journal impact and channels 
.                 
. *Three-way interaction
. est restore m5
{txt}(results {stata estimates replay m5:m5} are active now)

{com}. margins, dydx(jipf) at((p25)controls_total party_control=1) at( (p75)controls_total party_control=1)    post level(90)
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:jipf}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 14:party_control} = {res:{ralign 1:1}}
{lalign 7:}{space 0}{lalign 14:controls_total} = {res:{ralign 1:4}} {txt:(p25)}
{lalign 7:2._at: }{space 0}{lalign 14:party_control} = {res:{ralign 1:1}}
{lalign 7:}{space 0}{lalign 14:controls_total} = {res:{ralign 1:6}} {txt:(p75)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}jipf         {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1859927{col 26}{space 2} .0497727{col 37}{space 1}   -3.74{col 46}{space 3}0.000{col 54}{space 4}-.2678616{col 67}{space 3}-.1041239
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1271561{col 26}{space 2} .0561024{col 37}{space 1}   -2.27{col 46}{space 3}0.023{col 54}{space 4}-.2194363{col 67}{space 3}-.0348759
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}b[1,2]
          jipf:       jipf:
             1.          2.
           _at         _at
r1 {res} -.18599272  -.12715611
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[2,2]
                 jipf:      jipf:
                    1.         2.
                  _at        _at
jipf:1._at {res} .00247732
{txt}jipf:2._at {res} .00198333  .00314748
{reset}
{com}. 
. gen graph_id=_n in 1/3
{txt}(390 missing values generated)

{com}. gen graph_x=graph_id-1
{txt}(390 missing values generated)

{com}. 
. gen effect=.
{txt}(393 missing values generated)

{com}. gen upper=.
{txt}(393 missing values generated)

{com}. gen lower=.
{txt}(393 missing values generated)

{com}. 
. forval i=1/2 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.96*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.96*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. twoway ||  scatter effect graph_x, yaxis(1) yscale(range(-0.5 0.5)  axis(1))   ///
>                 title("",size(medsmall)) ///
>                 ytitle("Marginal effect on partisan effects", size(medsmall) axis(1))  ///
>             ylabel(-0.6(0.2)0.6, angle(0) nogrid) msymbol(o) ///
>                 xscale(range(-0.5 1.5)) xtitle("") ///
>                 xlabel(0  `" "Channels" "(p25)" "' 1  `" "Channels" "(p75)" "',nogrid) ///
>                 mcolor(black) ///
>         || rcap upper lower graph_x, yaxis(1) ///
>                 lcolor(black) ysize(4) xsize(5) ///
>                 graphregion(color(white)) ///
>                 legend(off) ///
>                 yline(0, lpattern(dash)) name(gr2, replace) ///
> 
{res}{txt}
{com}. drop graph_id graph_x effect upper lower
{txt}
{com}.                         
. cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export "Fig_SB2.png", width(3600) replace                 
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_SB2.png{rm}
saved as
PNG
format
{p_end}

{com}. cd  "$DATADIR"  
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *Figure S-B3: Test statistics, status of partisan effects and journal impact (median split)
. 
. *Median split of journals based on their impact factor:
. xtile jipf_2 = jipf, nq(2)
{txt}
{com}.  
. *Binning
. tab tvalue

    {txt}T-value {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        -10 {c |}{res}          3        0.77        0.77
{txt}       -7.2 {c |}{res}          1        0.26        1.03
{txt}       -6.1 {c |}{res}          1        0.26        1.29
{txt}         -6 {c |}{res}          1        0.26        1.55
{txt}       -5.4 {c |}{res}          1        0.26        1.80
{txt}       -4.6 {c |}{res}          1        0.26        2.06
{txt}       -4.1 {c |}{res}          1        0.26        2.32
{txt}         -4 {c |}{res}          2        0.52        2.84
{txt}       -3.9 {c |}{res}          2        0.52        3.35
{txt}       -3.8 {c |}{res}          2        0.52        3.87
{txt}       -3.7 {c |}{res}          1        0.26        4.12
{txt}       -3.5 {c |}{res}          1        0.26        4.38
{txt}       -3.4 {c |}{res}          1        0.26        4.64
{txt}       -3.3 {c |}{res}          1        0.26        4.90
{txt}       -3.2 {c |}{res}          2        0.52        5.41
{txt}       -3.1 {c |}{res}          2        0.52        5.93
{txt}       -2.9 {c |}{res}          1        0.26        6.19
{txt}       -2.8 {c |}{res}          1        0.26        6.44
{txt}       -2.7 {c |}{res}          2        0.52        6.96
{txt}       -2.6 {c |}{res}          1        0.26        7.22
{txt}       -2.5 {c |}{res}          2        0.52        7.73
{txt}       -2.4 {c |}{res}          5        1.29        9.02
{txt}       -2.3 {c |}{res}          7        1.80       10.82
{txt}       -2.2 {c |}{res}          6        1.55       12.37
{txt}         -2 {c |}{res}         19        4.90       17.27
{txt}       -1.9 {c |}{res}          1        0.26       17.53
{txt}       -1.8 {c |}{res}          1        0.26       17.78
{txt}       -1.7 {c |}{res}          4        1.03       18.81
{txt}       -1.6 {c |}{res}          4        1.03       19.85
{txt}       -1.5 {c |}{res}          7        1.80       21.65
{txt}       -1.4 {c |}{res}          4        1.03       22.68
{txt}       -1.3 {c |}{res}          3        0.77       23.45
{txt}       -1.2 {c |}{res}          4        1.03       24.48
{txt}         -1 {c |}{res}          7        1.80       26.29
{txt}        -.9 {c |}{res}         11        2.84       29.12
{txt}        -.8 {c |}{res}          5        1.29       30.41
{txt}        -.7 {c |}{res}         10        2.58       32.99
{txt}        -.6 {c |}{res}          4        1.03       34.02
{txt}        -.5 {c |}{res}          6        1.55       35.57
{txt}        -.4 {c |}{res}          8        2.06       37.63
{txt}        -.3 {c |}{res}         10        2.58       40.21
{txt}        -.2 {c |}{res}         14        3.61       43.81
{txt}        -.1 {c |}{res}         19        4.90       48.71
{txt}          0 {c |}{res}         11        2.84       51.55
{txt}         .1 {c |}{res}          8        2.06       53.61
{txt}         .2 {c |}{res}          7        1.80       55.41
{txt}         .3 {c |}{res}          7        1.80       57.22
{txt}         .4 {c |}{res}         11        2.84       60.05
{txt}         .5 {c |}{res}          9        2.32       62.37
{txt}         .6 {c |}{res}          7        1.80       64.18
{txt}         .7 {c |}{res}          9        2.32       66.49
{txt}         .8 {c |}{res}          5        1.29       67.78
{txt}         .9 {c |}{res}          6        1.55       69.33
{txt}          1 {c |}{res}          7        1.80       71.13
{txt}        1.1 {c |}{res}          4        1.03       72.16
{txt}        1.2 {c |}{res}          2        0.52       72.68
{txt}        1.3 {c |}{res}          8        2.06       74.74
{txt}        1.4 {c |}{res}          5        1.29       76.03
{txt}        1.5 {c |}{res}          8        2.06       78.09
{txt}        1.6 {c |}{res}          7        1.80       79.90
{txt}        1.7 {c |}{res}          2        0.52       80.41
{txt}        1.8 {c |}{res}          3        0.77       81.19
{txt}        1.9 {c |}{res}          3        0.77       81.96
{txt}          2 {c |}{res}          4        1.03       82.99
{txt}        2.1 {c |}{res}          5        1.29       84.28
{txt}        2.2 {c |}{res}          3        0.77       85.05
{txt}        2.3 {c |}{res}          3        0.77       85.82
{txt}        2.4 {c |}{res}          1        0.26       86.08
{txt}        2.5 {c |}{res}          2        0.52       86.60
{txt}        2.6 {c |}{res}          6        1.55       88.14
{txt}        2.7 {c |}{res}          1        0.26       88.40
{txt}        2.8 {c |}{res}          3        0.77       89.18
{txt}        2.9 {c |}{res}          2        0.52       89.69
{txt}          3 {c |}{res}          2        0.52       90.21
{txt}        3.1 {c |}{res}          6        1.55       91.75
{txt}        3.2 {c |}{res}          4        1.03       92.78
{txt}        3.3 {c |}{res}          8        2.06       94.85
{txt}        3.4 {c |}{res}          3        0.77       95.62
{txt}        3.5 {c |}{res}          3        0.77       96.39
{txt}        3.6 {c |}{res}          1        0.26       96.65
{txt}        3.8 {c |}{res}          1        0.26       96.91
{txt}        3.9 {c |}{res}          2        0.52       97.42
{txt}        4.3 {c |}{res}          1        0.26       97.68
{txt}        4.4 {c |}{res}          1        0.26       97.94
{txt}        4.7 {c |}{res}          1        0.26       98.20
{txt}        4.8 {c |}{res}          1        0.26       98.45
{txt}        5.5 {c |}{res}          1        0.26       98.71
{txt}          6 {c |}{res}          1        0.26       98.97
{txt}        6.9 {c |}{res}          1        0.26       99.23
{txt}        7.6 {c |}{res}          1        0.26       99.48
{txt}        7.7 {c |}{res}          1        0.26       99.74
{txt}          8 {c |}{res}          1        0.26      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        388      100.00
{txt}
{com}. gen help=1
{txt}
{com}. bysort tvalue jipf_2: egen total_t=total(help)
{txt}
{com}. su total_t //1-18

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}total_t {c |}{res}        393    4.760814     3.47275          1         15
{txt}
{com}. 
. bysort tvalue jipf_2 party_control: egen total_t2=total(help)
{txt}
{com}. su total_t2 //1-18

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}total_t2 {c |}{res}        393    3.676845    3.365476          1         15
{txt}
{com}.  
.  
. *Colored 
. clonevar x = tvalue
{txt}(5 missing values generated)

{com}. replace x =  x - 0.05
{txt}(388 real changes made)

{com}. 
.  clonevar x1 = tvalue
{txt}(5 missing values generated)

{com}. replace x1 =  x + 0.05
{txt}(37 real changes made)

{com}. 
.  clonevar x2 = tvalue
{txt}(5 missing values generated)

{com}. replace x2 =  x - 0.02
{txt}(388 real changes made)

{com}. 
. 
. twoway (bar total_t2 x  if jipf_2==1 &(tvalue>-5 & tvalue<5) & party_control==0, xline(-1.96 1.96) xline(-1.645 1.645, lpattern(dash)) ylab(, nogrid) barwidth(0.3) xtitle(Test statistic) fcolor(green%10) fintensity(inten70)) ///
>  (bar total_t2 x1  if jipf_2==1 &(tvalue>-5 & tvalue<5) & party_control==1, title(Low impact) barwidth(0.3) fcolor(navy%10) fintensity(inten70) name(gr1, replace) ytitle(N) ///
>  legend(position(6) col(2) ///
>            order(  1 "IV" ///
>                     2 "Control")) )                                     
{res}{txt}
{com}.                                         
. twoway (bar total_t2 x if jipf_2==2 &(tvalue>-5 & tvalue<5) & party_control==0, title(High impact) barwidth(0.3) fcolor(green%10) fintensity(inten70) ylab(, nogrid) xline(-1.96 1.96) xline(-1.645 1.645, lpattern(dash)) xlabel(, nogrid)) ///
>  (bar total_t2 x2  if jipf_2==2 &(tvalue>-5 & tvalue<5)& party_control==1, barwidth(0.3) fcolor(navy%10) fintensity(70) xtitle(Test statistic) ytitle(N) ///
>  legend(position(6) col(2) ///
>            order(  1 "IV" ///
>                     2 "Control")) name(gr2, replace))
{res}{txt}
{com}.                                         
. graph combine gr1 gr2, col(1) xsize(3) ysize(4) xcommon ycommon
{p 0 4 2}
{txt}(note:  named style
thin not found in class
linestyle,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
thin not found in class
linestyle,  default attributes used)
{p_end}
{res}{txt}
{com}. cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export Fig_SB3.png, replace width(3600)
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_SB3.png{rm}
saved as
PNG
format
{p_end}

{com}. cd  "$DATADIR"          
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. 
. *****************************************************   
. **S-C: Additional analyses and robustness checks
. *****************************************************
. 
. *Table S-C1: Explaining partisan effects on inequality (separate models for each IV)
. est clear
{res}{txt}
{com}. melogit partyeffect c.goldenshare jipf n_obs  , vce(robust)  ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-245.66104}  
Iteration 1:{space 2}Log likelihood = {res:  -245.491}  
Iteration 2:{space 2}Log likelihood = {res:-245.49098}  
Iteration 3:{space 2}Log likelihood = {res:-245.49098}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-193.22015}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-193.22015}  
Iteration 1:{space 2}Log pseudolikelihood = {res:  -180.121}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-176.83918}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-175.99961}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-175.99005}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-175.99063}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-175.99064}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}3{txt}){col 67}={res}{col 70}     0.89
{txt}Log pseudolikelihood = {res}-175.99064{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.8267
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2} 2.088446{col 26}{space 2} 2.828322{col 37}{space 1}    0.74{col 46}{space 3}0.460{col 54}{space 4}-3.454964{col 67}{space 3} 7.631856
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} -.134912{col 26}{space 2} .3314857{col 37}{space 1}   -0.41{col 46}{space 3}0.684{col 54}{space 4}-.7846121{col 67}{space 3} .5147881
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2}  .000896{col 26}{space 2} .0022125{col 37}{space 1}    0.40{col 46}{space 3}0.685{col 54}{space 4}-.0034404{col 67}{space 3} .0052324
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.540443{col 26}{space 2} 1.018286{col 37}{space 1}   -1.51{col 46}{space 3}0.130{col 54}{space 4}-3.536248{col 67}{space 3} .4553615
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 9.904523{col 26}{space 2} 5.024894{col 54}{space 4} 3.664297{col 67}{space 3} 26.77173
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cla1
{txt}
{com}. 
. melogit partyeffect 1.toprest jipf  n_obs   , vce(robust)  ||paper_id: 
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-249.23181}  
Iteration 1:{space 2}Log likelihood = {res:-248.96721}  
Iteration 2:{space 2}Log likelihood = {res:-248.96708}  
Iteration 3:{space 2}Log likelihood = {res:-248.96708}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-189.51964}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-189.51964}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-174.69451}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-169.15377}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-167.20015}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-166.89095}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-166.90441}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-166.90672}  
Iteration 7:{space 2}Log pseudolikelihood = {res: -166.9072}  
Iteration 8:{space 2}Log pseudolikelihood = {res:-166.90735}  
Iteration 9:{space 2}Log pseudolikelihood = {res: -166.9074}  
Iteration 10:{space 1}Log pseudolikelihood = {res:-166.90741}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}3{txt}){col 67}={res}{col 70}     4.73
{txt}Log pseudolikelihood = {res}-166.90741{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.1927
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.toprest {c |}{col 14}{res}{space 2} 2.225984{col 26}{space 2} 1.053044{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .1620561{col 67}{space 3} 4.289911
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.2873948{col 26}{space 2} .3532523{col 37}{space 1}   -0.81{col 46}{space 3}0.416{col 54}{space 4}-.9797565{col 67}{space 3} .4049669
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0015165{col 26}{space 2} .0023198{col 37}{space 1}    0.65{col 46}{space 3}0.513{col 54}{space 4}-.0030302{col 67}{space 3} .0060633
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.140714{col 26}{space 2} 1.182756{col 37}{space 1}   -1.81{col 46}{space 3}0.070{col 54}{space 4}-4.458873{col 67}{space 3} .1774445
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 13.87273{col 26}{space 2} 8.733372{col 54}{space 4} 4.039248{col 67}{space 3} 47.64566
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cla2
{txt}
{com}. 
. local i=3
{txt}
{com}. foreach var of varlist  ineq_meas gini_gr ideol_cum party_control  {c -(}
{txt}  2{com}.         melogit partyeffect i.`var' jipf n_obs , vce(robust)   ||paper_id:
{txt}  3{com}.         est store m_cla`i'
{txt}  4{com}.         local i = `i' + 1
{txt}  5{com}. {c )-}
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-252.03413}  
Iteration 1:{space 2}Log likelihood = {res:-251.78258}  
Iteration 2:{space 2}Log likelihood = {res:-251.78252}  
Iteration 3:{space 2}Log likelihood = {res:-251.78252}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-192.60173}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-192.60173}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-179.07464}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-174.57884}  
Iteration 3:{space 2}Log pseudolikelihood = {res: -173.7936}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-173.76727}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-173.77026}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-173.77056}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-173.77065}  
Iteration 8:{space 2}Log pseudolikelihood = {res:-173.77067}  
Iteration 9:{space 2}Log pseudolikelihood = {res:-173.77068}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}4{txt}){col 67}={res}{col 70}     6.60
{txt}Log pseudolikelihood = {res}-173.77068{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.1585
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .3674731{col 26}{space 2} .4086368{col 37}{space 1}    0.90{col 46}{space 3}0.369{col 54}{space 4}-.4334402{col 67}{space 3} 1.168387
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.063799{col 26}{space 2}  .837672{col 37}{space 1}    2.46{col 46}{space 3}0.014{col 54}{space 4} .4219923{col 67}{space 3} 3.705606
{txt}{space 12} {c |}
{space 8}jipf {c |}{col 14}{res}{space 2}-.3219747{col 26}{space 2} .3334676{col 37}{space 1}   -0.97{col 46}{space 3}0.334{col 54}{space 4}-.9755591{col 67}{space 3} .3316097
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0011339{col 26}{space 2} .0022227{col 37}{space 1}    0.51{col 46}{space 3}0.610{col 54}{space 4}-.0032226{col 67}{space 3} .0054904
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.538078{col 26}{space 2} .9904853{col 37}{space 1}   -1.55{col 46}{space 3}0.120{col 54}{space 4}-3.479393{col 67}{space 3} .4032375
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2}  11.1971{col 26}{space 2}  5.82995{col 54}{space 4} 4.035651{col 67}{space 3} 31.06685
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-107.63175}  
Iteration 1:{space 2}Log likelihood = {res:-107.48642}  
Iteration 2:{space 2}Log likelihood = {res:-107.48623}  
Iteration 3:{space 2}Log likelihood = {res:-107.48623}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-89.591275}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-89.591275}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-85.537314}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-84.254925}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-84.067121}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-84.060823}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-84.060784}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-84.060785}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       188
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.8
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}4{txt}){col 67}={res}{col 70}     5.37
{txt}Log pseudolikelihood = {res}-84.060785{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.2515
{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .4534576{col 26}{space 2} 1.171583{col 37}{space 1}    0.39{col 46}{space 3}0.699{col 54}{space 4}-1.842802{col 67}{space 3} 2.749717
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} 1.230582{col 26}{space 2} .7342372{col 37}{space 1}    1.68{col 46}{space 3}0.094{col 54}{space 4} -.208496{col 67}{space 3} 2.669661
{txt}{space 12} {c |}
{space 8}jipf {c |}{col 14}{res}{space 2}-.7499705{col 26}{space 2} .7306467{col 37}{space 1}   -1.03{col 46}{space 3}0.305{col 54}{space 4}-2.182012{col 67}{space 3} .6820708
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2}-.0010798{col 26}{space 2}  .003224{col 37}{space 1}   -0.33{col 46}{space 3}0.738{col 54}{space 4}-.0073987{col 67}{space 3}  .005239
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1902697{col 26}{space 2} 1.553006{col 37}{space 1}   -0.12{col 46}{space 3}0.902{col 54}{space 4}-3.234106{col 67}{space 3} 2.853567
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 5.820555{col 26}{space 2} 4.200691{col 54}{space 4} 1.414662{col 67}{space 3} 23.94837
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-234.96078}  
Iteration 1:{space 2}Log likelihood = {res:-234.79272}  
Iteration 2:{space 2}Log likelihood = {res:-234.79265}  
Iteration 3:{space 2}Log likelihood = {res:-234.79265}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-188.22984}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-188.22984}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-176.96642}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-174.66213}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-174.33629}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-174.33369}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-174.33373}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-174.33372}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}3{txt}){col 67}={res}{col 70}     6.43
{txt}Log pseudolikelihood = {res}-174.33372{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0926
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.159455{col 26}{space 2} .9079673{col 37}{space 1}    2.38{col 46}{space 3}0.017{col 54}{space 4} .3798716{col 67}{space 3} 3.939038
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.2742959{col 26}{space 2} .3359233{col 37}{space 1}   -0.82{col 46}{space 3}0.414{col 54}{space 4}-.9326935{col 67}{space 3} .3841017
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0017078{col 26}{space 2} .0020929{col 37}{space 1}    0.82{col 46}{space 3}0.415{col 54}{space 4}-.0023943{col 67}{space 3}   .00581
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.880666{col 26}{space 2} .9748187{col 37}{space 1}   -1.93{col 46}{space 3}0.054{col 54}{space 4}-3.791275{col 67}{space 3} .0299439
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.587136{col 26}{space 2} 4.263209{col 54}{space 4} 3.245316{col 67}{space 3} 22.72164
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-244.47592}  
Iteration 1:{space 2}Log likelihood = {res:-244.33489}  
Iteration 2:{space 2}Log likelihood = {res:-244.33485}  
Iteration 3:{space 2}Log likelihood = {res:-244.33485}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-190.16792}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-190.16792}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-177.79841}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-175.31722}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-174.88403}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-174.88066}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-174.88053}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-174.88051}  
Iteration 7:{space 2}Log pseudolikelihood = {res: -174.8805}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}3{txt}){col 67}={res}{col 70}     4.59
{txt}Log pseudolikelihood = {res}-174.8805{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.2045
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.776657{col 26}{space 2} .8800934{col 37}{space 1}   -2.02{col 46}{space 3}0.044{col 54}{space 4}-3.501608{col 67}{space 3}-.0517052
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} -.171385{col 26}{space 2} .3167442{col 37}{space 1}   -0.54{col 46}{space 3}0.588{col 54}{space 4}-.7921923{col 67}{space 3} .4494222
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0022811{col 26}{space 2} .0023754{col 37}{space 1}    0.96{col 46}{space 3}0.337{col 54}{space 4}-.0023745{col 67}{space 3} .0069367
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6172289{col 26}{space 2} .9035641{col 37}{space 1}   -0.68{col 46}{space 3}0.495{col 54}{space 4}-2.388182{col 67}{space 3} 1.153724
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2}  8.70334{col 26}{space 2} 4.438172{col 54}{space 4} 3.203503{col 67}{space 3} 23.64541
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. melogit partyeffect c.controls_total jipf  n_obs   , vce(robust)  ||paper_id: 
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-245.40034}  
Iteration 1:{space 2}Log likelihood = {res:-245.17845}  
Iteration 2:{space 2}Log likelihood = {res:-245.17832}  
Iteration 3:{space 2}Log likelihood = {res:-245.17832}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-189.43358}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-189.43358}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-177.45742}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-174.06993}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-173.54904}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-173.53969}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-173.53998}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-173.54004}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-173.54006}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}3{txt}){col 67}={res}{col 70}     7.42
{txt}Log pseudolikelihood = {res}-173.54006{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0596
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
controls_t~l {c |}{col 14}{res}{space 2}-.3879429{col 26}{space 2} .1425461{col 37}{space 1}   -2.72{col 46}{space 3}0.006{col 54}{space 4}-.6673282{col 67}{space 3}-.1085576
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.2401629{col 26}{space 2} .3411883{col 37}{space 1}   -0.70{col 46}{space 3}0.481{col 54}{space 4}-.9088797{col 67}{space 3} .4285538
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0016795{col 26}{space 2} .0022278{col 37}{space 1}    0.75{col 46}{space 3}0.451{col 54}{space 4}-.0026869{col 67}{space 3} .0060459
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7879551{col 26}{space 2} 1.124629{col 37}{space 1}    0.70{col 46}{space 3}0.484{col 54}{space 4}-1.416276{col 67}{space 3} 2.992187
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.949942{col 26}{space 2} 4.627038{col 54}{space 4} 3.249054{col 67}{space 3} 24.65378
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cla7
{txt}
{com}. 
. 
. *Output:  
.  cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m_cla1 m_cla3 m_cla4 m_cla2 m_cla5 m_cla6 m_cla7  using Table_SC1.rtf, replace label  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle() nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff."  ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
>  n_obs "N of observations" controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest *ideol_cum *party_control controls_total)
{res}{txt}(output written to {browse  `"Table_SC1.rtf"'})

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. 
. *Table S-C2: Explaining partisan effects on inequality (OLS vs. Logit Multi-level regression) 
. est clear
{res}{txt}
{com}. 
. melogit partyeffect c.goldenshare i.ineq_meas   1.ideol_cum 1.party_control controls_total  c.jipf c.n_obs  , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-213.87763}  
Iteration 1:{space 2}Log likelihood = {res:-213.20556}  
Iteration 2:{space 2}Log likelihood = {res: -213.2045}  
Iteration 3:{space 2}Log likelihood = {res: -213.2045}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-177.85851}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-177.85851}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -169.6397}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-167.63009}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-167.37068}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-167.36746}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-167.36774}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-167.36777}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-167.36777}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    27.68
{txt}Log pseudolikelihood = {res}-167.36777{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0005
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-3.376713{col 26}{space 2}  3.05442{col 37}{space 1}   -1.11{col 46}{space 3}0.269{col 54}{space 4}-9.363268{col 67}{space 3} 2.609841
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .4640449{col 26}{space 2}  .366476{col 37}{space 1}    1.27{col 46}{space 3}0.205{col 54}{space 4}-.2542349{col 67}{space 3} 1.182325
{txt}{space 6}Share  {c |}{col 14}{res}{space 2}  2.31473{col 26}{space 2} .9244469{col 37}{space 1}    2.50{col 46}{space 3}0.012{col 54}{space 4} .5028476{col 67}{space 3} 4.126613
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2}   3.1677{col 26}{space 2} 1.037616{col 37}{space 1}    3.05{col 46}{space 3}0.002{col 54}{space 4}  1.13401{col 67}{space 3}  5.20139
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2} -1.98017{col 26}{space 2} .9760174{col 37}{space 1}   -2.03{col 46}{space 3}0.042{col 54}{space 4}-3.893129{col 67}{space 3}-.0672106
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2693911{col 26}{space 2}  .149748{col 37}{space 1}   -1.80{col 46}{space 3}0.072{col 54}{space 4}-.5628918{col 67}{space 3} .0241096
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6744827{col 26}{space 2} .3458302{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-1.352298{col 67}{space 3} .0033321
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0040931{col 26}{space 2} .0024814{col 37}{space 1}    1.65{col 46}{space 3}0.099{col 54}{space 4}-.0007702{col 67}{space 3} .0089565
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0281546{col 26}{space 2}  1.36443{col 37}{space 1}   -0.02{col 46}{space 3}0.984{col 54}{space 4}-2.702387{col 67}{space 3} 2.646078
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2}  7.18499{col 26}{space 2} 3.607698{col 54}{space 4} 2.685495{col 67}{space 3}  19.2233
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1b
{txt}
{com}.  
. melogit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-90.987765}  
Iteration 1:{space 2}Log likelihood = {res:-88.248259}  
Iteration 2:{space 2}Log likelihood = {res:-88.219698}  
Iteration 3:{space 2}Log likelihood = {res:-88.219697}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-81.648807}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-81.648807}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-80.188917}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-79.955931}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-79.946748}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-79.946731}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-79.946731}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       188
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.8
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    39.48
{txt}Log pseudolikelihood = {res}-79.946731{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-4.550399{col 26}{space 2} 4.236364{col 37}{space 1}   -1.07{col 46}{space 3}0.283{col 54}{space 4}-12.85352{col 67}{space 3} 3.752722
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .7103867{col 26}{space 2} 1.269799{col 37}{space 1}    0.56{col 46}{space 3}0.576{col 54}{space 4}-1.778374{col 67}{space 3} 3.199147
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} 1.000453{col 26}{space 2} .7109585{col 37}{space 1}    1.41{col 46}{space 3}0.159{col 54}{space 4}-.3930002{col 67}{space 3} 2.393906
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.065932{col 26}{space 2} 1.462413{col 37}{space 1}    1.41{col 46}{space 3}0.158{col 54}{space 4}-.8003457{col 67}{space 3} 4.932209
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-3.612916{col 26}{space 2} 1.872056{col 37}{space 1}   -1.93{col 46}{space 3}0.054{col 54}{space 4}-7.282078{col 67}{space 3}  .056246
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3193909{col 26}{space 2} .2077367{col 37}{space 1}   -1.54{col 46}{space 3}0.124{col 54}{space 4}-.7265473{col 67}{space 3} .0877654
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.3999904{col 26}{space 2}  .860415{col 37}{space 1}   -0.46{col 46}{space 3}0.642{col 54}{space 4}-2.086373{col 67}{space 3} 1.286392
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0084538{col 26}{space 2}  .004761{col 37}{space 1}    1.78{col 46}{space 3}0.076{col 54}{space 4}-.0008775{col 67}{space 3} .0177851
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1457716{col 26}{space 2} 2.072666{col 37}{space 1}    0.07{col 46}{space 3}0.944{col 54}{space 4} -3.91658{col 67}{space 3} 4.208123
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 3.139942{col 26}{space 2} 2.371739{col 54}{space 4} .7144483{col 67}{space 3} 13.79979
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2b 
{txt}
{com}. melogit partyeffect c.goldenshare toprest  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-210.06493}  
Iteration 1:{space 2}Log likelihood = {res:-209.47081}  
Iteration 2:{space 2}Log likelihood = {res:-209.46976}  
Iteration 3:{space 2}Log likelihood = {res:-209.46976}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-173.59674}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-173.59674}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-164.39458}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-161.12258}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-160.20778}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-160.12978}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-160.12905}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-160.12901}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-160.12901}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    18.46
{txt}Log pseudolikelihood = {res}-160.12901{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0101
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.261786{col 26}{space 2} 2.912911{col 37}{space 1}   -0.78{col 46}{space 3}0.437{col 54}{space 4}-7.970987{col 67}{space 3} 3.447416
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} 2.343037{col 26}{space 2} 1.081403{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .2235263{col 67}{space 3} 4.462547
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.215504{col 26}{space 2} 1.077868{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} 1.102922{col 67}{space 3} 5.328085
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.966817{col 26}{space 2} 1.073582{col 37}{space 1}   -1.83{col 46}{space 3}0.067{col 54}{space 4}-4.070999{col 67}{space 3} .1373662
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3404235{col 26}{space 2} .1654117{col 37}{space 1}   -2.06{col 46}{space 3}0.040{col 54}{space 4}-.6646245{col 67}{space 3}-.0162225
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6569371{col 26}{space 2} .3522677{col 37}{space 1}   -1.86{col 46}{space 3}0.062{col 54}{space 4}-1.347369{col 67}{space 3} .0334948
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0044522{col 26}{space 2} .0024775{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4}-.0004037{col 67}{space 3} .0093081
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.3659238{col 26}{space 2}  1.43627{col 37}{space 1}   -0.25{col 46}{space 3}0.799{col 54}{space 4} -3.18096{col 67}{space 3} 2.449113
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.247118{col 26}{space 2} 4.606258{col 54}{space 4} 2.759822{col 67}{space 3} 24.64469
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3b
{txt}
{com}. 
. *This one as OLS not, but the SEs are much smaller
. mixed partyeffect c.goldenshare i.ineq_meas   1.ideol_cum 1.party_control controls_total c.jipf c.n_obs , vce(robust) ||paper_id:
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-163.79114}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-163.79114}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 54}Number of obs{col 70} = {res}   393
{txt}Group variable: {res}paper_id{col 54}{txt}Number of groups{col 70} = {res}    43
{txt}{col 54}Obs per group:
{col 67}min = {res}     1
{txt}{col 67}avg = {res}   9.1
{txt}{col 67}max = {res}    37
{col 54}{txt}Wald chi2({res}8{txt}){col 70} = {res} 44.18
{txt}Log pseudolikelihood = {res}-163.79114{col 54}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-.1920508{col 26}{space 2} .3270702{col 37}{space 1}   -0.59{col 46}{space 3}0.557{col 54}{space 4}-.8330965{col 67}{space 3} .4489949
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .0758507{col 26}{space 2} .0499031{col 37}{space 1}    1.52{col 46}{space 3}0.129{col 54}{space 4}-.0219577{col 67}{space 3}  .173659
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} .2328681{col 26}{space 2} .1308686{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0236296{col 67}{space 3} .4893659
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} .3223938{col 26}{space 2} .1136594{col 37}{space 1}    2.84{col 46}{space 3}0.005{col 54}{space 4} .0996256{col 67}{space 3} .5451621
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.1504396{col 26}{space 2} .1045273{col 37}{space 1}   -1.44{col 46}{space 3}0.150{col 54}{space 4}-.3553094{col 67}{space 3} .0544301
{txt}controls_t~l {c |}{col 14}{res}{space 2} -.034003{col 26}{space 2} .0197854{col 37}{space 1}   -1.72{col 46}{space 3}0.086{col 54}{space 4}-.0727816{col 67}{space 3} .0047756
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.0755629{col 26}{space 2} .0352131{col 37}{space 1}   -2.15{col 46}{space 3}0.032{col 54}{space 4}-.1445793{col 67}{space 3}-.0065465
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0002293{col 26}{space 2} .0002011{col 37}{space 1}    1.14{col 46}{space 3}0.254{col 54}{space 4}-.0001649{col 67}{space 3} .0006234
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .530113{col 26}{space 2}  .165663{col 37}{space 1}    3.20{col 46}{space 3}0.001{col 54}{space 4} .2054195{col 67}{space 3} .8548065
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}paper_id{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33}  .093625{col 44} .0217854{col 58} .0593373{col 70} .1477256
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .1094477{col 44} .0183577{col 58} .0787834{col 70} .1520474
{txt}{hline 29}{c BT}{hline 48}

{com}.  est store m1a
{txt}
{com}. 
.  mixed partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs , vce(robust) ||paper_id:
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-76.422784}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-76.422784}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 54}Number of obs{col 70} = {res}   188
{txt}Group variable: {res}paper_id{col 54}{txt}Number of groups{col 70} = {res}    24
{txt}{col 54}Obs per group:
{col 67}min = {res}     1
{txt}{col 67}avg = {res}   7.8
{txt}{col 67}max = {res}    30
{col 54}{txt}Wald chi2({res}8{txt}){col 70} = {res} 55.89
{txt}Log pseudolikelihood = {res}-76.422784{col 54}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-.2355487{col 26}{space 2} .4846352{col 37}{space 1}   -0.49{col 46}{space 3}0.627{col 54}{space 4}-1.185416{col 67}{space 3} .7143188
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .0688053{col 26}{space 2} .1524603{col 37}{space 1}    0.45{col 46}{space 3}0.652{col 54}{space 4}-.2300115{col 67}{space 3} .3676221
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .1286184{col 26}{space 2} .0873025{col 37}{space 1}    1.47{col 46}{space 3}0.141{col 54}{space 4}-.0424914{col 67}{space 3} .2997281
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} .2072058{col 26}{space 2} .1537747{col 37}{space 1}    1.35{col 46}{space 3}0.178{col 54}{space 4}-.0941871{col 67}{space 3} .5085987
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.3141575{col 26}{space 2} .2227238{col 37}{space 1}   -1.41{col 46}{space 3}0.158{col 54}{space 4}-.7506882{col 67}{space 3} .1223731
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.0389056{col 26}{space 2}  .026752{col 37}{space 1}   -1.45{col 46}{space 3}0.146{col 54}{space 4}-.0913385{col 67}{space 3} .0135273
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.0541347{col 26}{space 2} .1007239{col 37}{space 1}   -0.54{col 46}{space 3}0.591{col 54}{space 4}-.2515498{col 67}{space 3} .1432804
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0005031{col 26}{space 2} .0002867{col 37}{space 1}    1.75{col 46}{space 3}0.079{col 54}{space 4}-.0000589{col 67}{space 3} .0010652
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5486039{col 26}{space 2} .2261234{col 37}{space 1}    2.43{col 46}{space 3}0.015{col 54}{space 4}   .10541{col 67}{space 3} .9917977
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}paper_id{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .0741554{col 44} .0296278{col 58} .0338888{col 70} .1622666
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .1076735{col 44} .0232328{col 58} .0705414{col 70} .1643515
{txt}{hline 29}{c BT}{hline 48}

{com}.  est store m2a 
{txt}
{com}.  
. mixed partyeffect c.goldenshare toprest  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs , vce(robust) ||paper_id:
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-153.03785}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-153.03785}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 54}Number of obs{col 70} = {res}   393
{txt}Group variable: {res}paper_id{col 54}{txt}Number of groups{col 70} = {res}    43
{txt}{col 54}Obs per group:
{col 67}min = {res}     1
{txt}{col 67}avg = {res}   9.1
{txt}{col 67}max = {res}    37
{col 54}{txt}Wald chi2({res}7{txt}){col 70} = {res} 36.57
{txt}Log pseudolikelihood = {res}-153.03785{col 54}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-.0146246{col 26}{space 2} .3358142{col 37}{space 1}   -0.04{col 46}{space 3}0.965{col 54}{space 4}-.6728085{col 67}{space 3} .6435592
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} .3054378{col 26}{space 2} .1326567{col 37}{space 1}    2.30{col 46}{space 3}0.021{col 54}{space 4} .0454354{col 67}{space 3} .5654403
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} .3147667{col 26}{space 2}  .107801{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .1034807{col 67}{space 3} .5260528
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.1265177{col 26}{space 2} .1240802{col 37}{space 1}   -1.02{col 46}{space 3}0.308{col 54}{space 4}-.3697104{col 67}{space 3}  .116675
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.0413344{col 26}{space 2} .0201161{col 37}{space 1}   -2.05{col 46}{space 3}0.040{col 54}{space 4}-.0807612{col 67}{space 3}-.0019076
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.0807741{col 26}{space 2} .0372901{col 37}{space 1}   -2.17{col 46}{space 3}0.030{col 54}{space 4}-.1538614{col 67}{space 3}-.0076869
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0002196{col 26}{space 2} .0001818{col 37}{space 1}    1.21{col 46}{space 3}0.227{col 54}{space 4}-.0001367{col 67}{space 3} .0005759
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4848196{col 26}{space 2} .1668383{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .1578226{col 67}{space 3} .8118166
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}paper_id{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .0987258{col 44} .0215507{col 58}  .064361{col 70} .1514393
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .1025332{col 44}  .016473{col 58}  .074836{col 70} .1404813
{txt}{hline 29}{c BT}{hline 48}

{com}.  est store m3a
{txt}
{com}.  
. *Output:  
.  cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a m1b m2a m2b m3a m3b  using Table_SC2.rtf, replace label compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1 (OLS)" "M1 (Logit)" "M2 (OLS)" "M2 (Logit)" "M3 (OLS)" "M3 (Logit)") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff."  ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest *ideol_cum *party_control controls_total)
{res}{txt}(output written to {browse  `"Table_SC2.rtf"'})

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *Table S-C3: Explaining partisan effects on inequality (only articles with at least 5 results) 
. bysort paper_id: gen cluster_c=_N
{txt}
{com}. 
. est clear
{res}{txt}
{com}. 
. melogit partyeffect c.goldenshare i.ineq_meas   1.ideol_cum 1.party_control  controls_total c.jipf c.n_obs   if cluster_c>=5, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-194.47962}  
Iteration 1:{space 2}Log likelihood = {res:-193.52232}  
Iteration 2:{space 2}Log likelihood = {res: -193.5202}  
Iteration 3:{space 2}Log likelihood = {res: -193.5202}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-159.07886}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-159.07886}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-152.05865}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-150.31064}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-149.99557}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-149.98626}  
Iteration 5:{space 2}Log pseudolikelihood = {res: -149.9862}  
Iteration 6:{space 2}Log pseudolikelihood = {res: -149.9862}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       353
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         5
{col 63}{txt}avg{col 67}={res}{col 69}      13.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    31.76
{txt}Log pseudolikelihood = {res}-149.9862{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0001
{txt}{ralign 78:(Std. err. adjusted for {res:27} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-9.172937{col 26}{space 2} 3.609857{col 37}{space 1}   -2.54{col 46}{space 3}0.011{col 54}{space 4}-16.24813{col 67}{space 3}-2.097746
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .2748831{col 26}{space 2} .4789559{col 37}{space 1}    0.57{col 46}{space 3}0.566{col 54}{space 4}-.6638533{col 67}{space 3} 1.213619
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.496245{col 26}{space 2} .9002617{col 37}{space 1}    2.77{col 46}{space 3}0.006{col 54}{space 4}  .731764{col 67}{space 3} 4.260725
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.849424{col 26}{space 2} 1.146018{col 37}{space 1}    3.36{col 46}{space 3}0.001{col 54}{space 4}  1.60327{col 67}{space 3} 6.095577
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.864295{col 26}{space 2}  1.19196{col 37}{space 1}   -1.56{col 46}{space 3}0.118{col 54}{space 4}-4.200493{col 67}{space 3} .4719031
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2549414{col 26}{space 2} .1551326{col 37}{space 1}   -1.64{col 46}{space 3}0.100{col 54}{space 4}-.5589958{col 67}{space 3}  .049113
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.8862839{col 26}{space 2} .3863766{col 37}{space 1}   -2.29{col 46}{space 3}0.022{col 54}{space 4}-1.643568{col 67}{space 3}-.1289998
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0042704{col 26}{space 2} .0028475{col 37}{space 1}    1.50{col 46}{space 3}0.134{col 54}{space 4}-.0013105{col 67}{space 3} .0098513
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7055585{col 26}{space 2}  1.59416{col 37}{space 1}    0.44{col 46}{space 3}0.658{col 54}{space 4}-2.418937{col 67}{space 3} 3.830054
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 5.867909{col 26}{space 2} 2.797032{col 54}{space 4} 2.305391{col 67}{space 3} 14.93558
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1a
{txt}
{com}. 
. melogit partyeffect c.goldenshare toprest  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs  if cluster_c>=5, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-191.86374}  
Iteration 1:{space 2}Log likelihood = {res:-191.09756}  
Iteration 2:{space 2}Log likelihood = {res:-191.09576}  
Iteration 3:{space 2}Log likelihood = {res:-191.09576}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-156.27714}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-156.27714}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-148.47547}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.34425}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.01769}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.01188}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-145.01168}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-145.01164}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-145.01163}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       353
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        27

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         5
{col 63}{txt}avg{col 67}={res}{col 69}      13.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    17.92
{txt}Log pseudolikelihood = {res}-145.01163{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0123
{txt}{ralign 78:(Std. err. adjusted for {res:27} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-7.848013{col 26}{space 2}  3.47618{col 37}{space 1}   -2.26{col 46}{space 3}0.024{col 54}{space 4} -14.6612{col 67}{space 3}-1.034825
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} 2.156054{col 26}{space 2} 1.104385{col 37}{space 1}    1.95{col 46}{space 3}0.051{col 54}{space 4}-.0085003{col 67}{space 3} 4.320608
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.573306{col 26}{space 2}  1.19034{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} 1.240282{col 67}{space 3} 5.906329
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2} -2.03281{col 26}{space 2} 1.243695{col 37}{space 1}   -1.63{col 46}{space 3}0.102{col 54}{space 4}-4.470406{col 67}{space 3} .4047867
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2928946{col 26}{space 2} .1685912{col 37}{space 1}   -1.74{col 46}{space 3}0.082{col 54}{space 4}-.6233273{col 67}{space 3} .0375382
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.7516158{col 26}{space 2}   .40848{col 37}{space 1}   -1.84{col 46}{space 3}0.066{col 54}{space 4}-1.552222{col 67}{space 3} .0489904
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0043285{col 26}{space 2} .0028202{col 37}{space 1}    1.53{col 46}{space 3}0.125{col 54}{space 4}-.0011991{col 67}{space 3}  .009856
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4566935{col 26}{space 2} 1.683498{col 37}{space 1}    0.27{col 46}{space 3}0.786{col 54}{space 4}-2.842901{col 67}{space 3} 3.756288
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 7.416229{col 26}{space 2} 4.293187{col 54}{space 4} 2.384681{col 67}{space 3} 23.06407
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3a
{txt}
{com}.  
. melogit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs  if cluster_c>=5 , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-77.289233}  
Iteration 1:{space 2}Log likelihood = {res:-75.112062}  
Iteration 2:{space 2}Log likelihood = {res:-75.083078}  
Iteration 3:{space 2}Log likelihood = {res:-75.083066}  
Iteration 4:{space 2}Log likelihood = {res:-75.083066}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-71.427544}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-71.427544}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-71.004708}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-70.966414}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-70.966294}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-70.966295}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       165
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        15

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      11.0
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    67.54
{txt}Log pseudolikelihood = {res}-70.966295{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:15} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-7.177481{col 26}{space 2} 5.139288{col 37}{space 1}   -1.40{col 46}{space 3}0.163{col 54}{space 4} -17.2503{col 67}{space 3} 2.895339
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .4983188{col 26}{space 2} 1.306008{col 37}{space 1}    0.38{col 46}{space 3}0.703{col 54}{space 4}-2.061409{col 67}{space 3} 3.058047
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .7331084{col 26}{space 2} .6139242{col 37}{space 1}    1.19{col 46}{space 3}0.232{col 54}{space 4}-.4701609{col 67}{space 3} 1.936378
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.286929{col 26}{space 2} 1.448327{col 37}{space 1}    1.58{col 46}{space 3}0.114{col 54}{space 4}-.5517391{col 67}{space 3} 5.125598
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-4.264606{col 26}{space 2} 1.910864{col 37}{space 1}   -2.23{col 46}{space 3}0.026{col 54}{space 4}-8.009831{col 67}{space 3}-.5193805
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2563415{col 26}{space 2} .1858384{col 37}{space 1}   -1.38{col 46}{space 3}0.168{col 54}{space 4}-.6205782{col 67}{space 3} .1078951
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} -1.33302{col 26}{space 2} 1.292536{col 37}{space 1}   -1.03{col 46}{space 3}0.302{col 54}{space 4}-3.866344{col 67}{space 3} 1.200304
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0113713{col 26}{space 2} .0055304{col 37}{space 1}    2.06{col 46}{space 3}0.040{col 54}{space 4}  .000532{col 67}{space 3} .0222106
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.572912{col 26}{space 2}  2.44958{col 37}{space 1}    0.64{col 46}{space 3}0.521{col 54}{space 4}-3.228177{col 67}{space 3} 6.374002
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 2.012548{col 26}{space 2} 2.121116{col 54}{space 4} .2550506{col 67}{space 3} 15.88058
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2a 
{txt}
{com}. 
. melogit partyeffect c.goldenshare i.ineq_meas   1.ideol_cum 1.party_control controls_total  c.jipf c.n_obs   if cluster_c>=10, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res: -134.7277}  
Iteration 1:{space 2}Log likelihood = {res:-133.44225}  
Iteration 2:{space 2}Log likelihood = {res:-133.41824}  
Iteration 3:{space 2}Log likelihood = {res: -133.4182}  
Iteration 4:{space 2}Log likelihood = {res: -133.4182}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-118.57546}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-118.57546}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-116.75084}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-116.48645}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-116.47528}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-116.47529}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-116.47529}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       278
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        16

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        10
{col 63}{txt}avg{col 67}={res}{col 69}      17.4
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    29.84
{txt}Log pseudolikelihood = {res}-116.47529{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0002
{txt}{ralign 78:(Std. err. adjusted for {res:16} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-1.156468{col 26}{space 2} 3.564713{col 37}{space 1}   -0.32{col 46}{space 3}0.746{col 54}{space 4}-8.143177{col 67}{space 3} 5.830241
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .1874048{col 26}{space 2} .5261277{col 37}{space 1}    0.36{col 46}{space 3}0.722{col 54}{space 4}-.8437865{col 67}{space 3} 1.218596
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.214917{col 26}{space 2} 1.049346{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .1582358{col 67}{space 3} 4.271597
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 4.355769{col 26}{space 2} 1.374014{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4}  1.66275{col 67}{space 3} 7.048787
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.3798697{col 26}{space 2} 1.514616{col 37}{space 1}   -0.25{col 46}{space 3}0.802{col 54}{space 4}-3.348462{col 67}{space 3} 2.588722
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2843047{col 26}{space 2} .1491937{col 37}{space 1}   -1.91{col 46}{space 3}0.057{col 54}{space 4} -.576719{col 67}{space 3} .0081096
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-1.639091{col 26}{space 2} .6330179{col 37}{space 1}   -2.59{col 46}{space 3}0.010{col 54}{space 4}-2.879783{col 67}{space 3}-.3983985
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0043228{col 26}{space 2} .0026908{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4} -.000951{col 67}{space 3} .0095967
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.8367435{col 26}{space 2} 1.819583{col 37}{space 1}   -0.46{col 46}{space 3}0.646{col 54}{space 4} -4.40306{col 67}{space 3} 2.729573
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 2.668445{col 26}{space 2} 1.166931{col 54}{space 4} 1.132456{col 67}{space 3} 6.287746
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1b
{txt}
{com}. 
. melogit partyeffect c.goldenshare toprest  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs  if cluster_c>=10, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-133.87819}  
Iteration 1:{space 2}Log likelihood = {res: -132.1753}  
Iteration 2:{space 2}Log likelihood = {res:-132.14757}  
Iteration 3:{space 2}Log likelihood = {res:-132.14756}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-115.42702}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-115.42702}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-112.39136}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-111.67366}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-111.58383}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-111.58245}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-111.58247}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-111.58247}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       278
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        16

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        10
{col 63}{txt}avg{col 67}={res}{col 69}      17.4
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    26.84
{txt}Log pseudolikelihood = {res}-111.58247{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0004
{txt}{ralign 78:(Std. err. adjusted for {res:16} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-1.543326{col 26}{space 2}  4.06007{col 37}{space 1}   -0.38{col 46}{space 3}0.704{col 54}{space 4}-9.500918{col 67}{space 3} 6.414266
{txt}{space 5}toprest {c |}{col 14}{res}{space 2}   2.0867{col 26}{space 2} 1.161548{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4} -.189892{col 67}{space 3} 4.363291
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 4.446952{col 26}{space 2}  1.43565{col 37}{space 1}    3.10{col 46}{space 3}0.002{col 54}{space 4} 1.633129{col 67}{space 3} 7.260774
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.7058113{col 26}{space 2} 1.635932{col 37}{space 1}   -0.43{col 46}{space 3}0.666{col 54}{space 4}-3.912178{col 67}{space 3} 2.500556
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2837878{col 26}{space 2} .1600262{col 37}{space 1}   -1.77{col 46}{space 3}0.076{col 54}{space 4}-.5974333{col 67}{space 3} .0298578
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-1.465892{col 26}{space 2} .6533231{col 37}{space 1}   -2.24{col 46}{space 3}0.025{col 54}{space 4}-2.746382{col 67}{space 3}-.1854027
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0053699{col 26}{space 2}  .002844{col 37}{space 1}    1.89{col 46}{space 3}0.059{col 54}{space 4}-.0002042{col 67}{space 3} .0109441
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.568316{col 26}{space 2} 2.238844{col 37}{space 1}   -0.70{col 46}{space 3}0.484{col 54}{space 4}-5.956369{col 67}{space 3} 2.819737
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 3.789118{col 26}{space 2} 2.357886{col 54}{space 4} 1.119061{col 67}{space 3} 12.82987
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3b
{txt}
{com}.  
. melogit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs  if cluster_c>=10 , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-57.613654}  
Iteration 1:{space 2}Log likelihood = {res:-55.899452}  
Iteration 2:{space 2}Log likelihood = {res:-55.874295}  
Iteration 3:{space 2}Log likelihood = {res: -55.87428}  
Iteration 4:{space 2}Log likelihood = {res: -55.87428}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-53.029522}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-53.029522}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-51.871777}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-51.753667}  
Iteration 3:{space 2}Log pseudolikelihood = {res: -51.74934}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-51.749339}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       138
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        11

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}      12.5
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   122.25
{txt}Log pseudolikelihood = {res}-51.749339{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:11} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2} .2728345{col 26}{space 2} 8.141595{col 37}{space 1}    0.03{col 46}{space 3}0.973{col 54}{space 4} -15.6844{col 67}{space 3} 16.23007
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2}-1.517474{col 26}{space 2} 1.358584{col 37}{space 1}   -1.12{col 46}{space 3}0.264{col 54}{space 4}-4.180251{col 67}{space 3} 1.145302
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .8839876{col 26}{space 2} .3707125{col 37}{space 1}    2.38{col 46}{space 3}0.017{col 54}{space 4} .1574045{col 67}{space 3} 1.610571
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 5.272581{col 26}{space 2} 1.967061{col 37}{space 1}    2.68{col 46}{space 3}0.007{col 54}{space 4} 1.417213{col 67}{space 3}  9.12795
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.369531{col 26}{space 2} 4.069933{col 37}{space 1}   -0.58{col 46}{space 3}0.560{col 54}{space 4}-10.34645{col 67}{space 3} 5.607391
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2244081{col 26}{space 2} .2397516{col 37}{space 1}   -0.94{col 46}{space 3}0.349{col 54}{space 4}-.6943125{col 67}{space 3} .2454964
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-2.404232{col 26}{space 2}  1.31235{col 37}{space 1}   -1.83{col 46}{space 3}0.067{col 54}{space 4} -4.97639{col 67}{space 3} .1679262
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0149396{col 26}{space 2} .0056481{col 37}{space 1}    2.65{col 46}{space 3}0.008{col 54}{space 4} .0038697{col 67}{space 3} .0260096
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.518793{col 26}{space 2} 2.715077{col 37}{space 1}   -0.56{col 46}{space 3}0.576{col 54}{space 4}-6.840246{col 67}{space 3} 3.802661
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 2.235345{col 26}{space 2} 1.490916{col 54}{space 4} .6048053{col 67}{space 3} 8.261779
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2b  
{txt}
{com}.  
. *Output:  
. cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a m1b m2a m2b m3a m3b using Table_SC3.rtf, replace  label  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1 (CL>=5)" "M1 (CL>=10)" "M2 (CL>=5)" "M2 (CL>=10)" "M3 (CL>=5)" "M3 (CL>=10)" "M4 (CL>=5)" "M4 (CL>=10)" "M5 (CL>=5)" "M5 (CL>=10)" "M6 (CL>=5)" "M6 (CL>=10)") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff."  ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" controls_total "Number of policy channels" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age"  ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest *ideol_cum *party_control controls_total)
{res}{txt}(output written to {browse  `"Table_SC3.rtf"'})

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.  
.  
. *Table S-C4: Explaining partisan effects on inequality (excluding counterintuitive effects) 
. est clear
{res}{txt}
{com}. 
. melogit partyeffect c.goldenshare 1.party_control i.ineq_meas   1.ideol_cum  controls_total  c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-213.87763}  
Iteration 1:{space 2}Log likelihood = {res:-213.20556}  
Iteration 2:{space 2}Log likelihood = {res: -213.2045}  
Iteration 3:{space 2}Log likelihood = {res: -213.2045}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-177.85851}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-177.85851}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -169.6397}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-167.63009}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-167.37068}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-167.36746}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-167.36774}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-167.36777}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-167.36777}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    27.68
{txt}Log pseudolikelihood = {res}-167.36777{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0005
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-3.376713{col 26}{space 2}  3.05442{col 37}{space 1}   -1.11{col 46}{space 3}0.269{col 54}{space 4}-9.363268{col 67}{space 3} 2.609841
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2} -1.98017{col 26}{space 2} .9760174{col 37}{space 1}   -2.03{col 46}{space 3}0.042{col 54}{space 4}-3.893129{col 67}{space 3}-.0672106
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .4640449{col 26}{space 2}  .366476{col 37}{space 1}    1.27{col 46}{space 3}0.205{col 54}{space 4}-.2542349{col 67}{space 3} 1.182325
{txt}{space 6}Share  {c |}{col 14}{res}{space 2}  2.31473{col 26}{space 2} .9244469{col 37}{space 1}    2.50{col 46}{space 3}0.012{col 54}{space 4} .5028476{col 67}{space 3} 4.126613
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2}   3.1677{col 26}{space 2} 1.037616{col 37}{space 1}    3.05{col 46}{space 3}0.002{col 54}{space 4}  1.13401{col 67}{space 3}  5.20139
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2693911{col 26}{space 2}  .149748{col 37}{space 1}   -1.80{col 46}{space 3}0.072{col 54}{space 4}-.5628918{col 67}{space 3} .0241096
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6744827{col 26}{space 2} .3458302{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-1.352298{col 67}{space 3} .0033321
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0040931{col 26}{space 2} .0024814{col 37}{space 1}    1.65{col 46}{space 3}0.099{col 54}{space 4}-.0007702{col 67}{space 3} .0089565
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0281546{col 26}{space 2}  1.36443{col 37}{space 1}   -0.02{col 46}{space 3}0.984{col 54}{space 4}-2.702387{col 67}{space 3} 2.646078
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2}  7.18499{col 26}{space 2} 3.607698{col 54}{space 4} 2.685495{col 67}{space 3}  19.2233
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl1
{txt}
{com}. melogit partyeffect c.goldenshare toprest  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-210.06493}  
Iteration 1:{space 2}Log likelihood = {res:-209.47081}  
Iteration 2:{space 2}Log likelihood = {res:-209.46976}  
Iteration 3:{space 2}Log likelihood = {res:-209.46976}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-173.59674}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-173.59674}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-164.39458}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-161.12258}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-160.20778}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-160.12978}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-160.12905}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-160.12901}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-160.12901}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    18.46
{txt}Log pseudolikelihood = {res}-160.12901{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0101
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.261786{col 26}{space 2} 2.912911{col 37}{space 1}   -0.78{col 46}{space 3}0.437{col 54}{space 4}-7.970987{col 67}{space 3} 3.447416
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} 2.343037{col 26}{space 2} 1.081403{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .2235263{col 67}{space 3} 4.462547
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.215504{col 26}{space 2} 1.077868{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} 1.102922{col 67}{space 3} 5.328085
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.966817{col 26}{space 2} 1.073582{col 37}{space 1}   -1.83{col 46}{space 3}0.067{col 54}{space 4}-4.070999{col 67}{space 3} .1373662
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3404235{col 26}{space 2} .1654117{col 37}{space 1}   -2.06{col 46}{space 3}0.040{col 54}{space 4}-.6646245{col 67}{space 3}-.0162225
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6569371{col 26}{space 2} .3522677{col 37}{space 1}   -1.86{col 46}{space 3}0.062{col 54}{space 4}-1.347369{col 67}{space 3} .0334948
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0044522{col 26}{space 2} .0024775{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4}-.0004037{col 67}{space 3} .0093081
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.3659238{col 26}{space 2}  1.43627{col 37}{space 1}   -0.25{col 46}{space 3}0.799{col 54}{space 4} -3.18096{col 67}{space 3} 2.449113
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.247118{col 26}{space 2} 4.606258{col 54}{space 4} 2.759822{col 67}{space 3} 24.64469
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl5
{txt}
{com}. melogit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-90.987765}  
Iteration 1:{space 2}Log likelihood = {res:-88.248259}  
Iteration 2:{space 2}Log likelihood = {res:-88.219698}  
Iteration 3:{space 2}Log likelihood = {res:-88.219697}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-81.648807}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-81.648807}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-80.188917}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-79.955931}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-79.946748}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-79.946731}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-79.946731}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       188
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.8
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    39.48
{txt}Log pseudolikelihood = {res}-79.946731{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-4.550399{col 26}{space 2} 4.236364{col 37}{space 1}   -1.07{col 46}{space 3}0.283{col 54}{space 4}-12.85352{col 67}{space 3} 3.752722
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .7103867{col 26}{space 2} 1.269799{col 37}{space 1}    0.56{col 46}{space 3}0.576{col 54}{space 4}-1.778374{col 67}{space 3} 3.199147
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} 1.000453{col 26}{space 2} .7109585{col 37}{space 1}    1.41{col 46}{space 3}0.159{col 54}{space 4}-.3930002{col 67}{space 3} 2.393906
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.065932{col 26}{space 2} 1.462413{col 37}{space 1}    1.41{col 46}{space 3}0.158{col 54}{space 4}-.8003457{col 67}{space 3} 4.932209
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-3.612916{col 26}{space 2} 1.872056{col 37}{space 1}   -1.93{col 46}{space 3}0.054{col 54}{space 4}-7.282078{col 67}{space 3}  .056246
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3193909{col 26}{space 2} .2077367{col 37}{space 1}   -1.54{col 46}{space 3}0.124{col 54}{space 4}-.7265473{col 67}{space 3} .0877654
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.3999904{col 26}{space 2}  .860415{col 37}{space 1}   -0.46{col 46}{space 3}0.642{col 54}{space 4}-2.086373{col 67}{space 3} 1.286392
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0084538{col 26}{space 2}  .004761{col 37}{space 1}    1.78{col 46}{space 3}0.076{col 54}{space 4}-.0008775{col 67}{space 3} .0177851
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1457716{col 26}{space 2} 2.072666{col 37}{space 1}    0.07{col 46}{space 3}0.944{col 54}{space 4} -3.91658{col 67}{space 3} 4.208123
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 3.139942{col 26}{space 2} 2.371739{col 54}{space 4} .7144483{col 67}{space 3} 13.79979
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl3
{txt}
{com}. 
. melogit partyeffect_exp c.goldenshare 1.party_control i.ineq_meas  controls_total 1.ideol_cum    c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-192.69634}  
Iteration 1:{space 2}Log likelihood = {res:-191.45747}  
Iteration 2:{space 2}Log likelihood = {res:-191.45554}  
Iteration 3:{space 2}Log likelihood = {res:-191.45554}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-154.05983}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-154.05983}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-142.68779}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-139.72761}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-139.05123}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-139.02804}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-139.02966}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-139.02999}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-139.03007}  
Iteration 8:{space 2}Log pseudolikelihood = {res:-139.03009}  
Iteration 9:{space 2}Log pseudolikelihood = {res: -139.0301}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       377
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       8.8
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    28.42
{txt}Log pseudolikelihood = {res}-139.0301{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0004
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}partyeffec~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.075223{col 26}{space 2} 2.992566{col 37}{space 1}   -0.69{col 46}{space 3}0.488{col 54}{space 4}-7.940543{col 67}{space 3} 3.790098
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.986014{col 26}{space 2} 1.152793{col 37}{space 1}   -2.59{col 46}{space 3}0.010{col 54}{space 4}-5.245447{col 67}{space 3}-.7265809
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .7174553{col 26}{space 2} .3016354{col 37}{space 1}    2.38{col 46}{space 3}0.017{col 54}{space 4} .1262607{col 67}{space 3}  1.30865
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.305998{col 26}{space 2} .8770417{col 37}{space 1}    2.63{col 46}{space 3}0.009{col 54}{space 4} .5870281{col 67}{space 3} 4.024968
{txt}{space 12} {c |}
controls_t~l {c |}{col 14}{res}{space 2}-.2464111{col 26}{space 2} .1546411{col 37}{space 1}   -1.59{col 46}{space 3}0.111{col 54}{space 4} -.549502{col 67}{space 3} .0566799
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.129424{col 26}{space 2} 1.106747{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .9602388{col 67}{space 3} 5.298608
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6041371{col 26}{space 2} .3623245{col 37}{space 1}   -1.67{col 46}{space 3}0.095{col 54}{space 4} -1.31428{col 67}{space 3} .1060058
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0043506{col 26}{space 2}  .002671{col 37}{space 1}    1.63{col 46}{space 3}0.103{col 54}{space 4}-.0008846{col 67}{space 3} .0095857
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.7434531{col 26}{space 2} 1.299053{col 37}{space 1}   -0.57{col 46}{space 3}0.567{col 54}{space 4} -3.28955{col 67}{space 3} 1.802644
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 10.96114{col 26}{space 2} 6.368626{col 54}{space 4} 3.509877{col 67}{space 3}   34.231
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl2
{txt}
{com}. melogit partyeffect_exp c.goldenshare toprest  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-187.24791}  
Iteration 1:{space 2}Log likelihood = {res:-186.08619}  
Iteration 2:{space 2}Log likelihood = {res: -186.0857}  
Iteration 3:{space 2}Log likelihood = {res: -186.0857}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-148.16624}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-148.16624}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-136.12933}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-132.05575}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-130.53282}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-130.34101}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-130.34138}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-130.34191}  
Iteration 7:{space 2}Log pseudolikelihood = {res:  -130.342}  
Iteration 8:{space 2}Log pseudolikelihood = {res:-130.34201}  
Iteration 9:{space 2}Log pseudolikelihood = {res:-130.34202}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       377
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       8.8
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    16.48
{txt}Log pseudolikelihood = {res}-130.34202{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0210
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}partyeffec~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-.8337743{col 26}{space 2} 2.842122{col 37}{space 1}   -0.29{col 46}{space 3}0.769{col 54}{space 4}-6.404231{col 67}{space 3} 4.736683
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} 2.539951{col 26}{space 2} 1.182377{col 37}{space 1}    2.15{col 46}{space 3}0.032{col 54}{space 4} .2225347{col 67}{space 3} 4.857366
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.172405{col 26}{space 2} 1.205706{col 37}{space 1}    2.63{col 46}{space 3}0.009{col 54}{space 4} .8092642{col 67}{space 3} 5.535547
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-3.099606{col 26}{space 2} 1.228285{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}   -5.507{col 67}{space 3}-.6922122
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3070853{col 26}{space 2} .1755032{col 37}{space 1}   -1.75{col 46}{space 3}0.080{col 54}{space 4}-.6510653{col 67}{space 3} .0368947
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6220514{col 26}{space 2} .3808949{col 37}{space 1}   -1.63{col 46}{space 3}0.102{col 54}{space 4}-1.368592{col 67}{space 3}  .124489
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2}  .004743{col 26}{space 2} .0027342{col 37}{space 1}    1.73{col 46}{space 3}0.083{col 54}{space 4}-.0006159{col 67}{space 3}  .010102
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.096336{col 26}{space 2} 1.393301{col 37}{space 1}   -0.79{col 46}{space 3}0.431{col 54}{space 4}-3.827155{col 67}{space 3} 1.634483
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 11.94432{col 26}{space 2} 7.207083{col 54}{space 4}  3.66062{col 67}{space 3} 38.97337
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl6
{txt}
{com}. melogit partyeffect_exp c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-74.517847}  
Iteration 1:{space 2}Log likelihood = {res:-67.619127}  
Iteration 2:{space 2}Log likelihood = {res:-67.387706}  
Iteration 3:{space 2}Log likelihood = {res:-67.387136}  
Iteration 4:{space 2}Log likelihood = {res:-67.387136}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-57.883548}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-57.883548}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-54.397957}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-53.077715}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-52.910717}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-52.898706}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-52.898106}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-52.898055}  
Iteration 7:{space 2}Log pseudolikelihood = {res: -52.89806}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       177
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.4
{col 63}{txt}max{col 67}={res}{col 69}        28

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    22.33
{txt}Log pseudolikelihood = {res}-52.89806{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0043
{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}partyeffec~p{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-4.419492{col 26}{space 2} 4.813536{col 37}{space 1}   -0.92{col 46}{space 3}0.359{col 54}{space 4}-13.85385{col 67}{space 3} 5.014865
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} 1.632204{col 26}{space 2}  1.35246{col 37}{space 1}    1.21{col 46}{space 3}0.227{col 54}{space 4}-1.018568{col 67}{space 3} 4.282977
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} 1.149563{col 26}{space 2} .7552637{col 37}{space 1}    1.52{col 46}{space 3}0.128{col 54}{space 4}-.3307265{col 67}{space 3} 2.629853
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.138584{col 26}{space 2}  1.78577{col 37}{space 1}    1.20{col 46}{space 3}0.231{col 54}{space 4}-1.361461{col 67}{space 3} 5.638629
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-7.158289{col 26}{space 2} 2.562234{col 37}{space 1}   -2.79{col 46}{space 3}0.005{col 54}{space 4}-12.18017{col 67}{space 3}-2.136403
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2893294{col 26}{space 2} .2925389{col 37}{space 1}   -0.99{col 46}{space 3}0.323{col 54}{space 4}-.8626951{col 67}{space 3} .2840363
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} .1269959{col 26}{space 2}   .97266{col 37}{space 1}    0.13{col 46}{space 3}0.896{col 54}{space 4}-1.779383{col 67}{space 3} 2.033375
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0122297{col 26}{space 2} .0060396{col 37}{space 1}    2.02{col 46}{space 3}0.043{col 54}{space 4} .0003922{col 67}{space 3} .0240672
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.680568{col 26}{space 2}  2.44711{col 37}{space 1}   -0.69{col 46}{space 3}0.492{col 54}{space 4}-6.476815{col 67}{space 3} 3.115679
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.403953{col 26}{space 2}     7.24{col 54}{space 4} 1.553009{col 67}{space 3} 45.47714
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl4
{txt}
{com}. 
. *Output:  
.  cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m_cl1 m_cl2 m_cl3 m_cl4 m_cl5 m_cl6 using Table_SC4.rtf, replace label compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle() nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff." ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
> n_obs "N of observations" controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest *ideol_cum *party_control controls_total) 
{res}{txt}(output written to {browse  `"Table_SC4.rtf"'})

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. 
. *Table S-C5: Explaining partisan effects on inequality (Logistic regression) 
. est clear
{res}{txt}
{com}. logit partyeffect c.goldenshare i.ineq_meas   1.ideol_cum 1.party_control  controls_total c.jipf c.n_obs  , cluster (paper_id)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-259.80203}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-214.57287}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-213.20925}  
Iteration 3:{space 2}Log pseudolikelihood = {res: -213.2045}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -213.2045}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:393}
{txt}{col 57}{lalign 13:Wald chi2({res:8})}{col 70} = {res}{ralign 6:27.94}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0005}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-213.2045}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1794}

{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.159029{col 26}{space 2} 2.300497{col 37}{space 1}   -0.94{col 46}{space 3}0.348{col 54}{space 4}-6.667919{col 67}{space 3} 2.349861
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .1035705{col 26}{space 2} .4907557{col 37}{space 1}    0.21{col 46}{space 3}0.833{col 54}{space 4}-.8582929{col 67}{space 3} 1.065434
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} .8618938{col 26}{space 2} .8580691{col 37}{space 1}    1.00{col 46}{space 3}0.315{col 54}{space 4}-.8198907{col 67}{space 3} 2.543678
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.228661{col 26}{space 2} .5921248{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4} 1.068118{col 67}{space 3} 3.389204
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.361901{col 26}{space 2} .8782776{col 37}{space 1}   -1.55{col 46}{space 3}0.121{col 54}{space 4}-3.083294{col 67}{space 3}  .359491
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.1548537{col 26}{space 2} .1259361{col 37}{space 1}   -1.23{col 46}{space 3}0.219{col 54}{space 4}-.4016839{col 67}{space 3} .0919764
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6539315{col 26}{space 2} .2784689{col 37}{space 1}   -2.35{col 46}{space 3}0.019{col 54}{space 4}-1.199721{col 67}{space 3}-.1081425
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0034128{col 26}{space 2} .0023929{col 37}{space 1}    1.43{col 46}{space 3}0.154{col 54}{space 4}-.0012773{col 67}{space 3} .0081029
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1276434{col 26}{space 2} 1.073739{col 37}{space 1}    0.12{col 46}{space 3}0.905{col 54}{space 4}-1.976847{col 67}{space 3} 2.232134
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1a
{txt}
{com}. 
. logit partyeffect c.goldenshare toprest  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs  , cluster(paper_id) 

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-259.80203}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-210.68661}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-209.47265}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-209.46976}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-209.46976}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:393}
{txt}{col 57}{lalign 13:Wald chi2({res:7})}{col 70} = {res}{ralign 6:33.41}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-209.46976}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1937}

{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.115186{col 26}{space 2}  2.19816{col 37}{space 1}   -0.96{col 46}{space 3}0.336{col 54}{space 4}  -6.4235{col 67}{space 3} 2.193128
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} .9080878{col 26}{space 2} .5400413{col 37}{space 1}    1.68{col 46}{space 3}0.093{col 54}{space 4}-.1503738{col 67}{space 3} 1.966549
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2}  2.16541{col 26}{space 2} .5624058{col 37}{space 1}    3.85{col 46}{space 3}0.000{col 54}{space 4} 1.063115{col 67}{space 3} 3.267705
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.346306{col 26}{space 2} .8506971{col 37}{space 1}   -1.58{col 46}{space 3}0.114{col 54}{space 4}-3.013641{col 67}{space 3} .3210298
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.1848602{col 26}{space 2} .1235017{col 37}{space 1}   -1.50{col 46}{space 3}0.134{col 54}{space 4}-.4269191{col 67}{space 3} .0571988
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6090685{col 26}{space 2} .2434975{col 37}{space 1}   -2.50{col 46}{space 3}0.012{col 54}{space 4}-1.086315{col 67}{space 3}-.1318223
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0035225{col 26}{space 2} .0023671{col 37}{space 1}    1.49{col 46}{space 3}0.137{col 54}{space 4} -.001117{col 67}{space 3} .0081619
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}   .02657{col 26}{space 2} 1.090299{col 37}{space 1}    0.02{col 46}{space 3}0.981{col 54}{space 4}-2.110378{col 67}{space 3} 2.163517
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3a
{txt}
{com}.  
. logit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs   ,cluster(paper_id)  

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-121.84331}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-91.296661}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-88.290331}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-88.219869}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-88.219697}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-88.219697}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:188}
{txt}{col 57}{lalign 13:Wald chi2({res:8})}{col 70} = {res}{ralign 6:36.86}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-88.219697}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.2760}

{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-5.817247{col 26}{space 2} 3.092502{col 37}{space 1}   -1.88{col 46}{space 3}0.060{col 54}{space 4}-11.87844{col 67}{space 3} .2439457
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} 1.264914{col 26}{space 2} .8772175{col 37}{space 1}    1.44{col 46}{space 3}0.149{col 54}{space 4}-.4544007{col 67}{space 3} 2.984229
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .8792948{col 26}{space 2} .7497192{col 37}{space 1}    1.17{col 46}{space 3}0.241{col 54}{space 4}-.5901279{col 67}{space 3} 2.348718
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 1.995131{col 26}{space 2} .9147645{col 37}{space 1}    2.18{col 46}{space 3}0.029{col 54}{space 4} .2022252{col 67}{space 3} 3.788036
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-4.394907{col 26}{space 2} 1.187511{col 37}{space 1}   -3.70{col 46}{space 3}0.000{col 54}{space 4}-6.722386{col 67}{space 3}-2.067429
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2483362{col 26}{space 2} .1426777{col 37}{space 1}   -1.74{col 46}{space 3}0.082{col 54}{space 4}-.5279794{col 67}{space 3} .0313069
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}  -.49351{col 26}{space 2}  .687316{col 37}{space 1}   -0.72{col 46}{space 3}0.473{col 54}{space 4}-1.840624{col 67}{space 3} .8536046
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0099667{col 26}{space 2} .0032945{col 37}{space 1}    3.03{col 46}{space 3}0.002{col 54}{space 4} .0035096{col 67}{space 3} .0164239
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2698155{col 26}{space 2} 1.555772{col 37}{space 1}    0.17{col 46}{space 3}0.862{col 54}{space 4}-2.779442{col 67}{space 3} 3.319073
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2a 
{txt}
{com}. 
. logit partyeffect c.goldenshare i.ineq_meas   1.ideol_cum 1.party_control controls_total  c.jipf c.n_obs  , vce (robust)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-259.80203}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-214.57287}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-213.20925}  
Iteration 3:{space 2}Log pseudolikelihood = {res: -213.2045}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -213.2045}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:393}
{txt}{col 57}{lalign 13:Wald chi2({res:8})}{col 70} = {res}{ralign 6:61.68}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 9:-213.2045}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1794}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.159029{col 26}{space 2} 1.173569{col 37}{space 1}   -1.84{col 46}{space 3}0.066{col 54}{space 4}-4.459181{col 67}{space 3} .1411236
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .1035705{col 26}{space 2} .2752868{col 37}{space 1}    0.38{col 46}{space 3}0.707{col 54}{space 4}-.4359817{col 67}{space 3} .6431228
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} .8618938{col 26}{space 2} .4128742{col 37}{space 1}    2.09{col 46}{space 3}0.037{col 54}{space 4} .0526751{col 67}{space 3} 1.671112
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.228661{col 26}{space 2} .3434838{col 37}{space 1}    6.49{col 46}{space 3}0.000{col 54}{space 4} 1.555445{col 67}{space 3} 2.901877
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.361901{col 26}{space 2} .4105715{col 37}{space 1}   -3.32{col 46}{space 3}0.001{col 54}{space 4}-2.166607{col 67}{space 3}-.5571961
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.1548537{col 26}{space 2} .0704336{col 37}{space 1}   -2.20{col 46}{space 3}0.028{col 54}{space 4} -.292901{col 67}{space 3}-.0168064
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6539315{col 26}{space 2} .1447214{col 37}{space 1}   -4.52{col 46}{space 3}0.000{col 54}{space 4}-.9375802{col 67}{space 3}-.3702828
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0034128{col 26}{space 2} .0009774{col 37}{space 1}    3.49{col 46}{space 3}0.000{col 54}{space 4} .0014972{col 67}{space 3} .0053284
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1276434{col 26}{space 2} .5263139{col 37}{space 1}    0.24{col 46}{space 3}0.808{col 54}{space 4}-.9039129{col 67}{space 3}   1.1592
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1b
{txt}
{com}. 
. logit partyeffect c.goldenshare toprest  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs  , vce (robust)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-259.80203}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-210.68661}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-209.47265}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-209.46976}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-209.46976}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:393}
{txt}{col 57}{lalign 13:Wald chi2({res:7})}{col 70} = {res}{ralign 6:73.45}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-209.46976}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1937}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.115186{col 26}{space 2} 1.140807{col 37}{space 1}   -1.85{col 46}{space 3}0.064{col 54}{space 4}-4.351126{col 67}{space 3} .1207541
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} .9080878{col 26}{space 2} .2496381{col 37}{space 1}    3.64{col 46}{space 3}0.000{col 54}{space 4} .4188061{col 67}{space 3} 1.397369
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2}  2.16541{col 26}{space 2} .3115538{col 37}{space 1}    6.95{col 46}{space 3}0.000{col 54}{space 4} 1.554776{col 67}{space 3} 2.776044
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.346306{col 26}{space 2}  .400326{col 37}{space 1}   -3.36{col 46}{space 3}0.001{col 54}{space 4} -2.13093{col 67}{space 3}-.5616812
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.1848602{col 26}{space 2} .0692288{col 37}{space 1}   -2.67{col 46}{space 3}0.008{col 54}{space 4}-.3205462{col 67}{space 3}-.0491742
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6090685{col 26}{space 2} .1198096{col 37}{space 1}   -5.08{col 46}{space 3}0.000{col 54}{space 4}-.8438911{col 67}{space 3} -.374246
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0035225{col 26}{space 2} .0009644{col 37}{space 1}    3.65{col 46}{space 3}0.000{col 54}{space 4} .0016322{col 67}{space 3} .0054128
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}   .02657{col 26}{space 2} .5038355{col 37}{space 1}    0.05{col 46}{space 3}0.958{col 54}{space 4}-.9609295{col 67}{space 3} 1.014069
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3b
{txt}
{com}.  
. logit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs   ,vce (robust)

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-121.84331}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-91.296661}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-88.290331}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-88.219869}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-88.219697}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-88.219697}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:188}
{txt}{col 57}{lalign 13:Wald chi2({res:8})}{col 70} = {res}{ralign 6:30.83}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0002}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-88.219697}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.2760}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-5.817247{col 26}{space 2} 2.128651{col 37}{space 1}   -2.73{col 46}{space 3}0.006{col 54}{space 4}-9.989325{col 67}{space 3}-1.645168
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} 1.264914{col 26}{space 2} .6757044{col 37}{space 1}    1.87{col 46}{space 3}0.061{col 54}{space 4}-.0594424{col 67}{space 3}  2.58927
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .8792948{col 26}{space 2} .6612791{col 37}{space 1}    1.33{col 46}{space 3}0.184{col 54}{space 4}-.4167883{col 67}{space 3} 2.175378
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 1.995131{col 26}{space 2} .5359671{col 37}{space 1}    3.72{col 46}{space 3}0.000{col 54}{space 4} .9446544{col 67}{space 3} 3.045607
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-4.394907{col 26}{space 2} 1.214527{col 37}{space 1}   -3.62{col 46}{space 3}0.000{col 54}{space 4}-6.775337{col 67}{space 3}-2.014478
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2483362{col 26}{space 2} .1308198{col 37}{space 1}   -1.90{col 46}{space 3}0.058{col 54}{space 4}-.5047383{col 67}{space 3} .0080658
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}  -.49351{col 26}{space 2}  .404844{col 37}{space 1}   -1.22{col 46}{space 3}0.223{col 54}{space 4} -1.28699{col 67}{space 3} .2999698
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0099667{col 26}{space 2}  .002475{col 37}{space 1}    4.03{col 46}{space 3}0.000{col 54}{space 4} .0051159{col 67}{space 3} .0148176
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2698155{col 26}{space 2} 1.045414{col 37}{space 1}    0.26{col 46}{space 3}0.796{col 54}{space 4}-1.779159{col 67}{space 3}  2.31879
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2b
{txt}
{com}. 
. *Output:  
.  cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a m1b m2a m2b m3a m3b using Table_SC5.rtf, replace label compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1 (CL)" "M1 (RSE)" "M2 (CL)" "M2 (RSE)" "M3 (CL)" "M3 (RSE)") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff."  ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest  *ideol_cum *party_control controls_total )
{res}{txt}(output written to {browse  `"Table_SC5.rtf"'})

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. * Table S-C6: Explaining partisan effects on inequality: Effect of policy channels
. 
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. est clear
{res}{txt}
{com}. 
. local i=1
{txt}
{com}. foreach var of varlist   corp_control policies_control postind_control glob_control   controls_total {c -(}
{txt}  2{com}.         melogit partyeffect c.`var'  n_obs c.jipf , vce(robust)  ||paper_id: 
{txt}  3{com}.         est store m_cl`i'
{txt}  4{com}.         local i = `i' + 1
{txt}  5{com}. {c )-}
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-246.92904}  
Iteration 1:{space 2}Log likelihood = {res:-246.79866}  
Iteration 2:{space 2}Log likelihood = {res:-246.79863}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-190.85501}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-190.85501}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-177.82044}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-175.21913}  
Iteration 3:{space 2}Log pseudolikelihood = {res: -174.7398}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -174.7357}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-174.73569}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}3{txt}){col 67}={res}{col 70}     4.11
{txt}Log pseudolikelihood = {res}-174.73569{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.2502
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
corp_control {c |}{col 14}{res}{space 2}-.6614258{col 26}{space 2} .3392558{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-1.326355{col 67}{space 3} .0035032
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2}  .001316{col 26}{space 2} .0021999{col 37}{space 1}    0.60{col 46}{space 3}0.550{col 54}{space 4}-.0029958{col 67}{space 3} .0056278
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.1622718{col 26}{space 2} .3472687{col 37}{space 1}   -0.47{col 46}{space 3}0.640{col 54}{space 4}-.8429061{col 67}{space 3} .5183624
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2761547{col 26}{space 2} 1.040689{col 37}{space 1}   -0.27{col 46}{space 3}0.791{col 54}{space 4}-2.315867{col 67}{space 3} 1.763558
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 9.620191{col 26}{space 2} 5.124507{col 54}{space 4} 3.386598{col 67}{space 3} 27.32775
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-252.79626}  
Iteration 1:{space 2}Log likelihood = {res:-252.56092}  
Iteration 2:{space 2}Log likelihood = {res:-252.56089}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-193.58005}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-193.58005}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-179.65381}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-176.74369}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-176.15996}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-176.15859}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-176.15915}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-176.15918}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-176.15919}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}3{txt}){col 67}={res}{col 70}     0.32
{txt}Log pseudolikelihood = {res}-176.15919{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.9571
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
policies_c~l {c |}{col 14}{res}{space 2}-.0449509{col 26}{space 2} .2473187{col 37}{space 1}   -0.18{col 46}{space 3}0.856{col 54}{space 4}-.5296867{col 67}{space 3}  .439785
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0011125{col 26}{space 2} .0022518{col 37}{space 1}    0.49{col 46}{space 3}0.621{col 54}{space 4} -.003301{col 67}{space 3}  .005526
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.1352291{col 26}{space 2} .3377683{col 37}{space 1}   -0.40{col 46}{space 3}0.689{col 54}{space 4}-.7972428{col 67}{space 3} .5267846
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.187689{col 26}{space 2} .9257332{col 37}{space 1}   -1.28{col 46}{space 3}0.200{col 54}{space 4}-3.002093{col 67}{space 3} .6267145
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 10.16402{col 26}{space 2} 5.199945{col 54}{space 4} 3.728966{col 67}{space 3} 27.70403
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-250.62089}  
Iteration 1:{space 2}Log likelihood = {res:-250.36429}  
Iteration 2:{space 2}Log likelihood = {res:-250.36422}  
Iteration 3:{space 2}Log likelihood = {res:-250.36422}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-192.57208}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-192.57208}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-179.16607}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-176.21685}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-175.64374}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-175.63947}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-175.63992}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-175.63993}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}3{txt}){col 67}={res}{col 70}     1.95
{txt}Log pseudolikelihood = {res}-175.63993{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.5828
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
postind_co~l {c |}{col 14}{res}{space 2}-.2552536{col 26}{space 2} .1936562{col 37}{space 1}   -1.32{col 46}{space 3}0.187{col 54}{space 4}-.6348127{col 67}{space 3} .1243055
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0010135{col 26}{space 2} .0021981{col 37}{space 1}    0.46{col 46}{space 3}0.645{col 54}{space 4}-.0032947{col 67}{space 3} .0053216
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.1456931{col 26}{space 2} .3296283{col 37}{space 1}   -0.44{col 46}{space 3}0.658{col 54}{space 4}-.7917527{col 67}{space 3} .5003666
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6879787{col 26}{space 2} .9644839{col 37}{space 1}   -0.71{col 46}{space 3}0.476{col 54}{space 4}-2.578332{col 67}{space 3} 1.202375
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2}  9.80811{col 26}{space 2} 5.097708{col 54}{space 4} 3.541427{col 67}{space 3} 27.16391
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-250.33395}  
Iteration 1:{space 2}Log likelihood = {res:-250.11435}  
Iteration 2:{space 2}Log likelihood = {res:-250.11431}  
Iteration 3:{space 2}Log likelihood = {res:-250.11431}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-191.81102}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-191.81102}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-178.65521}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-175.15454}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-174.43818}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-174.39877}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-174.39939}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-174.39935}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-174.39934}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}3{txt}){col 67}={res}{col 70}     7.50
{txt}Log pseudolikelihood = {res}-174.39934{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0576
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
glob_control {c |}{col 14}{res}{space 2}  -.81663{col 26}{space 2} .3017149{col 37}{space 1}   -2.71{col 46}{space 3}0.007{col 54}{space 4} -1.40798{col 67}{space 3}-.2252796
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0015958{col 26}{space 2} .0022692{col 37}{space 1}    0.70{col 46}{space 3}0.482{col 54}{space 4}-.0028518{col 67}{space 3} .0060433
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.1784097{col 26}{space 2} .3392227{col 37}{space 1}   -0.53{col 46}{space 3}0.599{col 54}{space 4} -.843274{col 67}{space 3} .4864547
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -.498184{col 26}{space 2} .9045947{col 37}{space 1}   -0.55{col 46}{space 3}0.582{col 54}{space 4}-2.271157{col 67}{space 3} 1.274789
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 9.880923{col 26}{space 2} 4.807502{col 54}{space 4} 3.807594{col 67}{space 3} 25.64156
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-245.40034}  
Iteration 1:{space 2}Log likelihood = {res:-245.17845}  
Iteration 2:{space 2}Log likelihood = {res:-245.17832}  
Iteration 3:{space 2}Log likelihood = {res:-245.17832}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-189.43358}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-189.43358}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-177.45742}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-174.06993}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-173.54904}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-173.53969}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-173.53998}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-173.54004}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-173.54006}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}3{txt}){col 67}={res}{col 70}     7.42
{txt}Log pseudolikelihood = {res}-173.54006{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0596
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
controls_t~l {c |}{col 14}{res}{space 2}-.3879429{col 26}{space 2} .1425461{col 37}{space 1}   -2.72{col 46}{space 3}0.006{col 54}{space 4}-.6673282{col 67}{space 3}-.1085576
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0016795{col 26}{space 2} .0022278{col 37}{space 1}    0.75{col 46}{space 3}0.451{col 54}{space 4}-.0026869{col 67}{space 3} .0060459
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.2401629{col 26}{space 2} .3411883{col 37}{space 1}   -0.70{col 46}{space 3}0.481{col 54}{space 4}-.9088797{col 67}{space 3} .4285538
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7879551{col 26}{space 2} 1.124629{col 37}{space 1}    0.70{col 46}{space 3}0.484{col 54}{space 4}-1.416276{col 67}{space 3} 2.992187
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.949942{col 26}{space 2} 4.627038{col 54}{space 4} 3.249054{col 67}{space 3} 24.65378
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m_cl* using Table_SC6.rtf, replace label compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle("Model 1" "Model 2" "Model 3" "Model 4" "Model 5" "Model 6") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff." ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
>  n_obs "N of observations" controls_total "Number of policy channels" ///
>    policies_control "policies_control" corp_control "Corporatism"  ///
> postind_control "Postindustrialization" glob_control "Globalization" controls_total "Total N of channels" ///
>  n_obs "N of observations"  _cons "Constant") //
{res}{txt}(output written to {browse  `"Table_SC6.rtf"'})

{com}.  order(*corp_control  *policies_control  *postind* *glob* controls_total *jipf *n_obs)
{txt}
{com}. cd  "$DATADIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.  
. * Figure SC1: Marginal effect of different policy channels on partisan effects
. gen graph_id=_n in 1/5
{txt}(388 missing values generated)

{com}. gen graph_x=graph_id-1
{txt}(388 missing values generated)

{com}. 
. gen effect=.
{txt}(393 missing values generated)

{com}. gen upper=.
{txt}(393 missing values generated)

{com}. gen lower=.
{txt}(393 missing values generated)

{com}.  
.  
.  *Corporatism
.  est restore m_cl1
{txt}(results {stata estimates replay m_cl1:m_cl1} are active now)

{com}.  margins, dydx(corp_control) pwcompare level(90) //-6%
{txt}{p 0 6 2}note: ignoring pwcompare options because there are no margins for making pairwise comparisons.{p_end}
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:corp_control}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
corp_control {c |}{col 14}{res}{space 2}-.0686732{col 26}{space 2} .0367915{col 37}{space 1}   -1.87{col 46}{space 3}0.062{col 54}{space 4}-.1291898{col 67}{space 3}-.0081566
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
    corp_control
r1 {res}    -.0686732
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
              corp_control
corp_control {res}    .00135361
{reset}
{com}. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}.  
. *policies_control
. est restore m_cl2
{txt}(results {stata estimates replay m_cl2:m_cl2} are active now)

{com}. margins,dydx(policies_control) pwcompare  post level (90) //0.00
{txt}{p 0 6 2}note: ignoring pwcompare options because there are no margins for making pairwise comparisons.{p_end}
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:policies_control}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
policies_c~l {c |}{col 14}{res}{space 2}-.0045766{col 26}{space 2} .0252085{col 37}{space 1}   -0.18{col 46}{space 3}0.856{col 54}{space 4}-.0460409{col 67}{space 3} .0368876
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
    policies_c~l
r1 {res}   -.00457663
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
              policies_c~l
policies_c~l {res}    .00063547
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}.  
. *Endo
.    est restore m_cl3
{txt}(results {stata estimates replay m_cl3:m_cl3} are active now)

{com}. margins, dydx(postind_control) level (90) //-0.00
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:postind_control}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
postind_co~l {c |}{col 14}{res}{space 2}-.0258858{col 26}{space 2} .0210945{col 37}{space 1}   -1.23{col 46}{space 3}0.220{col 54}{space 4}-.0605832{col 67}{space 3} .0088116
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
    postind_co~l
r1 {res}   -.02588584
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
              postind_co~l
postind_co~l {res}    .00044498
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. *Exo
.    est restore m_cl4
{txt}(results {stata estimates replay m_cl4:m_cl4} are active now)

{com}.  margins, dydx(glob_control) pwcompare level (90) //-7%
{txt}{p 0 6 2}note: ignoring pwcompare options because there are no margins for making pairwise comparisons.{p_end}
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:glob_control}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
glob_control {c |}{col 14}{res}{space 2}-.0838469{col 26}{space 2} .0333499{col 37}{space 1}   -2.51{col 46}{space 3}0.012{col 54}{space 4}-.1387027{col 67}{space 3}-.0289912
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
    glob_control
r1 {res}   -.08384693
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
              glob_control
glob_control {res}    .00111222
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+2
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. *Policy channels - total N
.    est restore m_cl5
{txt}(results {stata estimates replay m_cl5:m_cl5} are active now)

{com}.  margins, dydx(controls_total)  level (90) //-3%
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:controls_total}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
controls_t~l {c |}{col 14}{res}{space 2}-.0402113{col 26}{space 2} .0170981{col 37}{space 1}   -2.35{col 46}{space 3}0.019{col 54}{space 4}-.0683352{col 67}{space 3}-.0120874
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
    controls_t~l
r1 {res}    -.0402113
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
              controls_t~l
controls_t~l {res}    .00029235
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+3
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. * twoway plot with addplot option
. twoway ||  scatter effect graph_x, yaxis(1) yscale(range(0 0.3)  axis(1))   ///
>                 ytitle("Marginal effect on partisan effects", size(medsmall) axis(1))  ///
>             ylabel(-0.3(0.1)0.3, angle(0) nogrid) msymbol(o) ///
>                 xscale(range(-0.5 4.5)) ///
>                 xlabel( 0 "Corp" 1 "Pol" 2 "Post" 3 "Glob" 4 "Total", nogrid) ///
>                 mcolor(black)  ///
>         || rcap upper lower graph_x, yaxis(1) ///
>                 lcolor(black) ysize(3) xsize(4) ///
>                 graphregion(color(white)) xtitle("") ///
>                 legend(off) title("Policy channels",size(medsmall)) ///  xtitle("") ///
>                 yline(0, lpattern(dash)) name(gr2, replace) //
{res}{txt}
{com}.  cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export "Fig_SC1.png", width(3600) replace                 
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_SC1.png{rm}
saved as
PNG
format
{p_end}

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.                 
. drop graph_id graph_x effect upper lower  
{txt}
{com}.  
. *Table S-C7: Explaining partisan effects on inequality: Temporal effect of policy channels
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. gen short_n=policies_control
{txt}
{com}. gen long_n=controls_total-policies_control
{txt}
{com}. 
. su short_n long_n

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}short_n {c |}{res}        393    .7811705    .9678251          0          5
{txt}{space 6}long_n {c |}{res}        393    4.493639    1.927403          0          8
{txt}
{com}. 
. est clear
{res}{txt}
{com}. 
. melogit partyeffect  i.ineq_meas ib0.ideol_cum##c.short_n 1.party_control  c.jipf c.n_obs goldenshare  , vce(robust) ||paper_id:        
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-215.83841}  
Iteration 1:{space 2}Log likelihood = {res:-215.14334}  
Iteration 2:{space 2}Log likelihood = {res:-215.14285}  
Iteration 3:{space 2}Log likelihood = {res:-215.14285}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-179.42629}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-179.42629}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-170.68939}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-167.94733}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-167.30564}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-167.26882}  
Iteration 5:{space 2}Log pseudolikelihood = {res: -167.2692}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-167.26921}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}    26.72
{txt}Log pseudolikelihood = {res}-167.26921{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0016
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .5069936{col 26}{space 2} .4013527{col 37}{space 1}    1.26{col 46}{space 3}0.207{col 54}{space 4}-.2796432{col 67}{space 3}  1.29363
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.480845{col 26}{space 2}  .908871{col 37}{space 1}    2.73{col 46}{space 3}0.006{col 54}{space 4}  .699491{col 67}{space 3}   4.2622
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.626862{col 26}{space 2} .9967996{col 37}{space 1}    2.64{col 46}{space 3}0.008{col 54}{space 4} .6731705{col 67}{space 3} 4.580553
{txt}{space 5}short_n {c |}{col 14}{res}{space 2}-.2982239{col 26}{space 2}  .337879{col 37}{space 1}   -0.88{col 46}{space 3}0.377{col 54}{space 4}-.9604547{col 67}{space 3} .3640068
{txt}{space 12} {c |}
{space 3}ideol_cum#{c |}
{space 3}c.short_n {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 1.165971{col 26}{space 2} .4674723{col 37}{space 1}    2.49{col 46}{space 3}0.013{col 54}{space 4} .2497424{col 67}{space 3}   2.0822
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2} -2.73887{col 26}{space 2} .8921129{col 37}{space 1}   -3.07{col 46}{space 3}0.002{col 54}{space 4}-4.487379{col 67}{space 3}-.9903607
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6623628{col 26}{space 2} .3356248{col 37}{space 1}   -1.97{col 46}{space 3}0.048{col 54}{space 4}-1.320175{col 67}{space 3}-.0045502
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0047029{col 26}{space 2}  .002558{col 37}{space 1}    1.84{col 46}{space 3}0.066{col 54}{space 4}-.0003107{col 67}{space 3} .0097165
{txt}{space 1}goldenshare {c |}{col 14}{res}{space 2}-3.582091{col 26}{space 2} 2.933708{col 37}{space 1}   -1.22{col 46}{space 3}0.222{col 54}{space 4}-9.332053{col 67}{space 3}  2.16787
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.107892{col 26}{space 2} 1.195787{col 37}{space 1}   -0.93{col 46}{space 3}0.354{col 54}{space 4}-3.451592{col 67}{space 3} 1.235807
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 7.519919{col 26}{space 2} 3.581429{col 54}{space 4} 2.956792{col 67}{space 3} 19.12518
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl1
{txt}
{com}.         
. melogit partyeffect  i.ineq_meas ib0.ideol_cum##c.long_n 1.party_control  c.jipf c.n_obs goldenshare  , vce(robust) ||paper_id: 
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-213.30818}  
Iteration 1:{space 2}Log likelihood = {res:-212.65685}  
Iteration 2:{space 2}Log likelihood = {res:-212.65564}  
Iteration 3:{space 2}Log likelihood = {res:-212.65564}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-177.71553}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-177.71553}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-169.58359}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-167.60895}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-167.34891}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-167.34568}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-167.34591}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-167.34594}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-167.34594}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}    27.90
{txt}Log pseudolikelihood = {res}-167.34594{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0010
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .4479805{col 26}{space 2} .3800178{col 37}{space 1}    1.18{col 46}{space 3}0.238{col 54}{space 4}-.2968407{col 67}{space 3} 1.192802
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.357676{col 26}{space 2} .9233361{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} .5479705{col 67}{space 3} 4.167381
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.507907{col 26}{space 2} 1.378224{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} .8066379{col 67}{space 3} 6.209176
{txt}{space 6}long_n {c |}{col 14}{res}{space 2}-.2086652{col 26}{space 2} .1819049{col 37}{space 1}   -1.15{col 46}{space 3}0.251{col 54}{space 4}-.5651922{col 67}{space 3} .1478618
{txt}{space 12} {c |}
{space 3}ideol_cum#{c |}
{space 4}c.long_n {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0754524{col 26}{space 2} .2469186{col 37}{space 1}   -0.31{col 46}{space 3}0.760{col 54}{space 4}-.5594039{col 67}{space 3} .4084991
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.132692{col 26}{space 2} .9068539{col 37}{space 1}   -2.35{col 46}{space 3}0.019{col 54}{space 4}-3.910093{col 67}{space 3}-.3552913
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6506323{col 26}{space 2} .3384665{col 37}{space 1}   -1.92{col 46}{space 3}0.055{col 54}{space 4}-1.314014{col 67}{space 3} .0127498
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0040245{col 26}{space 2} .0025295{col 37}{space 1}    1.59{col 46}{space 3}0.112{col 54}{space 4}-.0009332{col 67}{space 3} .0089822
{txt}{space 1}goldenshare {c |}{col 14}{res}{space 2}-3.328274{col 26}{space 2} 3.074648{col 37}{space 1}   -1.08{col 46}{space 3}0.279{col 54}{space 4}-9.354474{col 67}{space 3} 2.697926
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.4645132{col 26}{space 2} 1.450955{col 37}{space 1}   -0.32{col 46}{space 3}0.749{col 54}{space 4}-3.308332{col 67}{space 3} 2.379306
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 6.998941{col 26}{space 2}  3.55263{col 54}{space 4} 2.588013{col 67}{space 3} 18.92772
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl2
{txt}
{com}. 
. melogit partyeffect  1.toprest ib0.ideol_cum##c.short_n 1.party_control  c.jipf c.n_obs goldenshare  , vce(robust) ||paper_id:  
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-213.02633}  
Iteration 1:{space 2}Log likelihood = {res: -212.4198}  
Iteration 2:{space 2}Log likelihood = {res:-212.41893}  
Iteration 3:{space 2}Log likelihood = {res:-212.41893}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-175.46436}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-175.46436}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-164.91199}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-161.27245}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-160.16614}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-160.04169}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-160.04244}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-160.04263}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-160.04263}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    17.06
{txt}Log pseudolikelihood = {res}-160.04263{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0295
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.toprest {c |}{col 14}{res}{space 2} 2.433918{col 26}{space 2} 1.074454{col 37}{space 1}    2.27{col 46}{space 3}0.023{col 54}{space 4}  .328027{col 67}{space 3} 4.539808
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2}  2.51514{col 26}{space 2} 1.044706{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .4675536{col 67}{space 3} 4.562726
{txt}{space 5}short_n {c |}{col 14}{res}{space 2}-.3895019{col 26}{space 2}  .401657{col 37}{space 1}   -0.97{col 46}{space 3}0.332{col 54}{space 4}-1.176735{col 67}{space 3} .3977313
{txt}{space 12} {c |}
{space 3}ideol_cum#{c |}
{space 3}c.short_n {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 1.507787{col 26}{space 2} .5783192{col 37}{space 1}    2.61{col 46}{space 3}0.009{col 54}{space 4} .3743026{col 67}{space 3} 2.641272
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.950433{col 26}{space 2} 1.014347{col 37}{space 1}   -2.91{col 46}{space 3}0.004{col 54}{space 4}-4.938517{col 67}{space 3} -.962349
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6217767{col 26}{space 2} .3421226{col 37}{space 1}   -1.82{col 46}{space 3}0.069{col 54}{space 4}-1.292325{col 67}{space 3} .0487712
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0052435{col 26}{space 2} .0026069{col 37}{space 1}    2.01{col 46}{space 3}0.044{col 54}{space 4} .0001341{col 67}{space 3}  .010353
{txt}{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.493079{col 26}{space 2} 2.877195{col 37}{space 1}   -0.87{col 46}{space 3}0.386{col 54}{space 4}-8.132278{col 67}{space 3}  3.14612
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.747163{col 26}{space 2}   1.3387{col 37}{space 1}   -1.31{col 46}{space 3}0.192{col 54}{space 4}-4.370966{col 67}{space 3} .8766401
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 9.142145{col 26}{space 2} 4.896316{col 54}{space 4} 3.200109{col 67}{space 3} 26.11749
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl5
{txt}
{com}.         
. melogit partyeffect  1.toprest ib0.ideol_cum##c.long_n 1.party_control  c.jipf c.n_obs goldenshare  , vce(robust) ||paper_id:   
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res: -209.6913}  
Iteration 1:{space 2}Log likelihood = {res:-209.02155}  
Iteration 2:{space 2}Log likelihood = {res:-209.02033}  
Iteration 3:{space 2}Log likelihood = {res:-209.02033}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-173.36299}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-173.36299}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-164.33436}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-161.17618}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-160.30141}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-160.22951}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-160.23001}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-160.23012}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-160.23013}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    23.04
{txt}Log pseudolikelihood = {res}-160.23013{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0033
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.toprest {c |}{col 14}{res}{space 2} 2.340886{col 26}{space 2}  1.06658{col 37}{space 1}    2.19{col 46}{space 3}0.028{col 54}{space 4} .2504273{col 67}{space 3} 4.431344
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.135257{col 26}{space 2} 1.282766{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4} .6210816{col 67}{space 3} 5.649433
{txt}{space 6}long_n {c |}{col 14}{res}{space 2}-.3244332{col 26}{space 2} .1811361{col 37}{space 1}   -1.79{col 46}{space 3}0.073{col 54}{space 4}-.6794535{col 67}{space 3}  .030587
{txt}{space 12} {c |}
{space 3}ideol_cum#{c |}
{space 4}c.long_n {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  .020378{col 26}{space 2} .2482743{col 37}{space 1}    0.08{col 46}{space 3}0.935{col 54}{space 4}-.4662307{col 67}{space 3} .5069866
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.103201{col 26}{space 2} .9973856{col 37}{space 1}   -2.11{col 46}{space 3}0.035{col 54}{space 4}-4.058041{col 67}{space 3}-.1483613
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6164764{col 26}{space 2} .3387688{col 37}{space 1}   -1.82{col 46}{space 3}0.069{col 54}{space 4}-1.280451{col 67}{space 3} .0474983
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0042394{col 26}{space 2} .0024896{col 37}{space 1}    1.70{col 46}{space 3}0.089{col 54}{space 4}-.0006401{col 67}{space 3}  .009119
{txt}{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.046127{col 26}{space 2} 2.951347{col 37}{space 1}   -0.69{col 46}{space 3}0.488{col 54}{space 4}-7.830661{col 67}{space 3} 3.738407
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6788515{col 26}{space 2} 1.504949{col 37}{space 1}   -0.45{col 46}{space 3}0.652{col 54}{space 4}-3.628497{col 67}{space 3} 2.270794
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.126306{col 26}{space 2} 4.521822{col 54}{space 4} 2.730539{col 67}{space 3} 24.18454
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl6
{txt}
{com}. 
. melogit partyeffect  i.gini_gr ib0.ideol_cum##c.short_n 1.party_control  c.jipf c.n_obs goldenshare  , vce(robust) ||paper_id:  
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res: -90.98703}  
Iteration 1:{space 2}Log likelihood = {res:-87.932638}  
Iteration 2:{space 2}Log likelihood = {res: -87.88553}  
Iteration 3:{space 2}Log likelihood = {res:-87.885524}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-82.105718}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-82.105718}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-80.513817}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-80.204467}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-80.186934}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-80.186886}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-80.186886}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       188
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.8
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}    46.16
{txt}Log pseudolikelihood = {res}-80.186886{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .6395353{col 26}{space 2} 1.345301{col 37}{space 1}    0.48{col 46}{space 3}0.635{col 54}{space 4}-1.997207{col 67}{space 3} 3.276278
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .6687924{col 26}{space 2} .7910891{col 37}{space 1}    0.85{col 46}{space 3}0.398{col 54}{space 4}-.8817138{col 67}{space 3} 2.219299
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2}  1.36047{col 26}{space 2} 1.481568{col 37}{space 1}    0.92{col 46}{space 3}0.358{col 54}{space 4} -1.54335{col 67}{space 3} 4.264291
{txt}{space 5}short_n {c |}{col 14}{res}{space 2} -.064687{col 26}{space 2} .1962461{col 37}{space 1}   -0.33{col 46}{space 3}0.742{col 54}{space 4}-.4493222{col 67}{space 3} .3199482
{txt}{space 12} {c |}
{space 3}ideol_cum#{c |}
{space 3}c.short_n {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .9872236{col 26}{space 2} .4711044{col 37}{space 1}    2.10{col 46}{space 3}0.036{col 54}{space 4}  .063876{col 67}{space 3} 1.910571
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-5.204973{col 26}{space 2} 1.177709{col 37}{space 1}   -4.42{col 46}{space 3}0.000{col 54}{space 4} -7.51324{col 67}{space 3}-2.896705
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.1534086{col 26}{space 2} .7730507{col 37}{space 1}   -0.20{col 46}{space 3}0.843{col 54}{space 4} -1.66856{col 67}{space 3} 1.361743
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0092524{col 26}{space 2} .0050612{col 37}{space 1}    1.83{col 46}{space 3}0.068{col 54}{space 4}-.0006675{col 67}{space 3} .0191722
{txt}{space 1}goldenshare {c |}{col 14}{res}{space 2}-5.265776{col 26}{space 2} 4.193716{col 37}{space 1}   -1.26{col 46}{space 3}0.209{col 54}{space 4}-13.48531{col 67}{space 3} 2.953757
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.052049{col 26}{space 2} 1.778038{col 37}{space 1}   -0.59{col 46}{space 3}0.554{col 54}{space 4} -4.53694{col 67}{space 3} 2.432842
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 3.509168{col 26}{space 2} 2.992539{col 54}{space 4} .6596577{col 67}{space 3} 18.66765
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl3
{txt}
{com}.         
. melogit partyeffect  i.gini_gr ib0.ideol_cum##c.long_n 1.party_control  c.jipf c.n_obs goldenshare  , vce(robust) ||paper_id:   
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-91.364741}  
Iteration 1:{space 2}Log likelihood = {res: -88.29109}  
Iteration 2:{space 2}Log likelihood = {res:-88.251463}  
Iteration 3:{space 2}Log likelihood = {res:-88.251453}  
Iteration 4:{space 2}Log likelihood = {res:-88.251453}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-81.431775}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-81.431775}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-79.992676}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-79.767928}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-79.759323}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-79.759323}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       188
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.8
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}    56.51
{txt}Log pseudolikelihood = {res}-79.759323{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .5196001{col 26}{space 2} 1.260601{col 37}{space 1}    0.41{col 46}{space 3}0.680{col 54}{space 4}-1.951132{col 67}{space 3} 2.990332
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .9309234{col 26}{space 2} .7344659{col 37}{space 1}    1.27{col 46}{space 3}0.205{col 54}{space 4}-.5086032{col 67}{space 3}  2.37045
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} .8696726{col 26}{space 2} 1.989651{col 37}{space 1}    0.44{col 46}{space 3}0.662{col 54}{space 4}-3.029971{col 67}{space 3} 4.769317
{txt}{space 6}long_n {c |}{col 14}{res}{space 2}-.4314865{col 26}{space 2} .3647501{col 37}{space 1}   -1.18{col 46}{space 3}0.237{col 54}{space 4}-1.146384{col 67}{space 3} .2834106
{txt}{space 12} {c |}
{space 3}ideol_cum#{c |}
{space 4}c.long_n {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2766321{col 26}{space 2} .3815137{col 37}{space 1}    0.73{col 46}{space 3}0.468{col 54}{space 4} -.471121{col 67}{space 3} 1.024385
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-4.228724{col 26}{space 2} 1.658454{col 37}{space 1}   -2.55{col 46}{space 3}0.011{col 54}{space 4}-7.479235{col 67}{space 3}-.9782132
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.2661858{col 26}{space 2} .8262007{col 37}{space 1}   -0.32{col 46}{space 3}0.747{col 54}{space 4}-1.885509{col 67}{space 3} 1.353138
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0087325{col 26}{space 2} .0047689{col 37}{space 1}    1.83{col 46}{space 3}0.067{col 54}{space 4}-.0006144{col 67}{space 3} .0180793
{txt}{space 1}goldenshare {c |}{col 14}{res}{space 2}-4.459664{col 26}{space 2} 4.238229{col 37}{space 1}   -1.05{col 46}{space 3}0.293{col 54}{space 4}-12.76644{col 67}{space 3} 3.847111
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4830481{col 26}{space 2} 2.376081{col 37}{space 1}    0.20{col 46}{space 3}0.839{col 54}{space 4}-4.173986{col 67}{space 3} 5.140082
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 2.980769{col 26}{space 2} 2.089637{col 54}{space 4} .7544014{col 67}{space 3} 11.77753
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est store m_cl4
{txt}
{com}. 
. 
. cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m_cl1 m_cl2 m_cl3 m_cl4 m_cl5 m_cl6  using Table_SC7.rtf, replace label compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle("Model 1 (Short)" "Model 1 (long)" "Model 2 (short)" "Model 2 (long)" "Model 3 (short)" "Model 3 (long)") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff." ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
>  n_obs "N of observations" ib0.ideol_cum##c.long_n "Cumulative measure#Long-term channels" ///
>    policies_control "policies_control" corp_control "Corporatism" short_n "Immediate channels" long_n "Long-term channels" ib0.ideol_cum##c.short_n "Cumulative measure#Short-term policies_control" controls_total "Number of policy channels" ///
> postind_control "Postindustrialization" glob_control "Globalization" controls_total "Total N of channels" ///
>  n_obs "N of observations"  _cons "Constant") //
{res}{txt}(output written to {browse  `"Table_SC7.rtf"'})

{com}.  order( *goldenshare *ineq_meas *toprest *gini_gr *short_n *long_n  *ideol_cum *party_control *policies_control *corp_control *postind* *glob* )
{txt}
{com}.  cd  "$DATADIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *Figure S-C2: Marginal effect of policy channels on partisan effects 
. gen graph_id=_n in 1/5
{txt}(388 missing values generated)

{com}. gen graph_x=graph_id-1
{txt}(388 missing values generated)

{com}. 
. gen effect=.
{txt}(393 missing values generated)

{com}. gen upper=.
{txt}(393 missing values generated)

{com}. gen lower=.
{txt}(393 missing values generated)

{com}.  
.  *policies_control: short
.  est restore m_cl1
{txt}(results {stata estimates replay m_cl1:m_cl1} are active now)

{com}.  margins, dydx(short_n) at(ideol_cum=0) pwcompare level(90) //-3%
{txt}{p 0 6 2}note: ignoring pwcompare options because there are no margins for making pairwise comparisons.{p_end}
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:short_n}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 9:ideol_cum} = {res:{ralign 1:0}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}short_n {c |}{col 14}{res}{space 2}-.0243513{col 26}{space 2} .0265215{col 37}{space 1}   -0.92{col 46}{space 3}0.359{col 54}{space 4}-.0679752{col 67}{space 3} .0192727
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
       short_n
r1 {res} -.02435127
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           short_n
short_n {res} .00070339
{reset}
{com}. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'-1
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}.  
. *policies_control: long
. est restore m_cl1
{txt}(results {stata estimates replay m_cl1:m_cl1} are active now)

{com}.  margins, dydx(short_n) at(ideol_cum=1) pwcompare level(90) //+11%
{txt}{p 0 6 2}note: ignoring pwcompare options because there are no margins for making pairwise comparisons.{p_end}
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:short_n}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 9:ideol_cum} = {res:{ralign 1:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}short_n {c |}{col 14}{res}{space 2} .0934015{col 26}{space 2} .0325239{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0399044{col 67}{space 3} .1468986
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
     short_n
r1 {res} .0934015
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
          short_n
short_n {res} .0010578
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}.  
. *Channels: short
.    est restore m_cl2
{txt}(results {stata estimates replay m_cl2:m_cl2} are active now)

{com}.  margins, dydx(long_n) at(ideol_cum=0) pwcompare level(90) //-2%
{txt}{p 0 6 2}note: ignoring pwcompare options because there are no margins for making pairwise comparisons.{p_end}
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:long_n}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 9:ideol_cum} = {res:{ralign 1:0}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}long_n {c |}{col 14}{res}{space 2}-.0176171{col 26}{space 2} .0155064{col 37}{space 1}   -1.14{col 46}{space 3}0.256{col 54}{space 4}-.0431229{col 67}{space 3} .0078886
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        long_n
r1 {res} -.01761711
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           long_n
long_n {res} .00024045
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+2
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. *Channels: long
.    est restore m_cl2
{txt}(results {stata estimates replay m_cl2:m_cl2} are active now)

{com}.  margins, dydx(long_n) at(ideol_cum=1) pwcompare level(90) //-3%
{txt}{p 0 6 2}note: ignoring pwcompare options because there are no margins for making pairwise comparisons.{p_end}
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:393}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Marginal predicted mean, predict()}{p_end}
{p2col:dy/dx wrt:}{res:long_n}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 9:ideol_cum} = {res:{ralign 1:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [90% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}long_n {c |}{col 14}{res}{space 2}-.0314364{col 26}{space 2} .0197866{col 37}{space 1}   -1.59{col 46}{space 3}0.112{col 54}{space 4}-.0639825{col 67}{space 3} .0011096
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. matrix b=r(b)
{txt}
{com}. matrix V=r(V)
{txt}
{com}. matrix list b
{res}
{txt}symmetric b[1,1]
        long_n
r1 {res} -.03143642
{reset}
{com}. matrix list V
{res}
{txt}symmetric V[1,1]
           long_n
long_n {res} .00039151
{reset}
{com}. 
. forval i=1/1 {c -(}
{txt}  2{com}.         local j=`i'+3
{txt}  3{com}.         replace effect=b[1,`i']                                      if graph_x==`j'
{txt}  4{com}.         replace upper=b[1,`i'] + 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  5{com}.         replace lower=b[1,`i'] - 1.645*sqrt(V[`i',`i']) if graph_x==`j'
{txt}  6{com}. {c )-}
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. 
. * twoway plot with addplot option
. twoway ||  scatter effect graph_x, yaxis(1) yscale(range(0 0.3)  axis(1))   ///
>                 ytitle("Marginal effect on partisan effects", size(medsmall) axis(1))  ///
>             ylabel(-0.3(0.1)0.3, angle(0) nogrid) msymbol(o) ///
>                 xscale(range(-0.5 4.5)) ///
>                 xlabel( 0 "Short term" 1 "Cumulative" 3 "Short term" 4 "Cumulative", nogrid) ///
>                 text(0.25 0.5 "Short-term channels") ///
>                 text(0.25 3.5 "Long-term channels") ///
>                 mcolor(black)  ///
>         || rcap upper lower graph_x, yaxis(1) ///
>                 lcolor(black) ysize(3) xsize(4) ///
>                 graphregion(color(white)) xtitle("Partisan effects") ///
>                 legend(off) title("Long and short term effects of policy channels",size(medsmall)) ///   xtitle("") ///
>                 yline(0, lpattern(dash)) name(gr2, replace) //
{res}{txt}
{com}.  
. cd  "$GRAPHDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs
{txt}
{com}. graph export "Fig_SC2.png", width(3600) replace                 
{txt}{p 0 4 2}
file {bf}
/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/graphs/Fig_SC2.png{rm}
saved as
PNG
format
{p_end}

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.                 
.  drop graph_id graph_x effect upper lower  
{txt}
{com}.  
. * Table S-C8: Explaining partisan effects on inequality: Effect of different data sources
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. est clear
{res}{txt}
{com}. melogit partyeffect c.goldenshare 1.party_control i.ineq_meas   1.ideol_cum controls_total  i.datasource_ineq  c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-201.26717}  
Iteration 1:{space 2}Log likelihood = {res:-199.91768}  
Iteration 2:{space 2}Log likelihood = {res:-199.91417}  
Iteration 3:{space 2}Log likelihood = {res:-199.91417}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res: -174.0798}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -174.0798}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-168.18625}  
Iteration 2:{space 2}Log pseudolikelihood = {res:  -166.222}  
Iteration 3:{space 2}Log pseudolikelihood = {res:  -166.133}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-166.13318}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-166.13321}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-166.13321}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}12{txt}){col 67}={res}{col 70}    32.20
{txt}Log pseudolikelihood = {res}-166.13321{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0013
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.688269{col 26}{space 2} 2.888861{col 37}{space 1}   -0.93{col 46}{space 3}0.352{col 54}{space 4}-8.350333{col 67}{space 3} 2.973795
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.247997{col 26}{space 2} .9614935{col 37}{space 1}   -2.34{col 46}{space 3}0.019{col 54}{space 4} -4.13249{col 67}{space 3}-.3635046
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .3685753{col 26}{space 2} .4007831{col 37}{space 1}    0.92{col 46}{space 3}0.358{col 54}{space 4}-.4169452{col 67}{space 3} 1.154096
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 1.536158{col 26}{space 2} 1.157293{col 37}{space 1}    1.33{col 46}{space 3}0.184{col 54}{space 4}-.7320949{col 67}{space 3} 3.804411
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.284735{col 26}{space 2}  1.06622{col 37}{space 1}    3.08{col 46}{space 3}0.002{col 54}{space 4} 1.194982{col 67}{space 3} 5.374489
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2752125{col 26}{space 2} .1444165{col 37}{space 1}   -1.91{col 46}{space 3}0.057{col 54}{space 4}-.5582636{col 67}{space 3} .0078386
{txt}{space 12} {c |}
datasource~q {c |}
{space 7}OECD  {c |}{col 14}{res}{space 2} .7961498{col 26}{space 2} 1.440789{col 37}{space 1}    0.55{col 46}{space 3}0.581{col 54}{space 4}-2.027744{col 67}{space 3} 3.620044
{txt}{space 6}SWIID  {c |}{col 14}{res}{space 2} 1.705987{col 26}{space 2} 1.293804{col 37}{space 1}    1.32{col 46}{space 3}0.187{col 54}{space 4}-.8298215{col 67}{space 3} 4.241796
{txt}{space 8}WID  {c |}{col 14}{res}{space 2} 2.858636{col 26}{space 2} 2.199324{col 37}{space 1}    1.30{col 46}{space 3}0.194{col 54}{space 4} -1.45196{col 67}{space 3} 7.169232
{txt}{space 6}Other  {c |}{col 14}{res}{space 2} .1120249{col 26}{space 2} 1.470137{col 37}{space 1}    0.08{col 46}{space 3}0.939{col 54}{space 4}-2.769391{col 67}{space 3} 2.993441
{txt}{space 12} {c |}
{space 8}jipf {c |}{col 14}{res}{space 2}-.7648275{col 26}{space 2}  .372115{col 37}{space 1}   -2.06{col 46}{space 3}0.040{col 54}{space 4} -1.49416{col 67}{space 3}-.0354955
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0011862{col 26}{space 2} .0028242{col 37}{space 1}    0.42{col 46}{space 3}0.674{col 54}{space 4} -.004349{col 67}{space 3} .0067215
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0416961{col 26}{space 2} 1.444425{col 37}{space 1}    0.03{col 46}{space 3}0.977{col 54}{space 4}-2.789324{col 67}{space 3} 2.872717
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 5.825531{col 26}{space 2} 3.138281{col 54}{space 4} 2.026672{col 67}{space 3}  16.7451
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1a
{txt}
{com}. 
. melogit partyeffect c.goldenshare toprest  1.ideol_cum 1.party_control controls_total i.datasource_ineq c.jipf c.n_obs   , vce(robust) ||paper_id: 
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-198.30857}  
Iteration 1:{space 2}Log likelihood = {res:-197.00235}  
Iteration 2:{space 2}Log likelihood = {res:-196.99997}  
Iteration 3:{space 2}Log likelihood = {res:-196.99997}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-169.59476}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-169.59476}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-161.81542}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-159.23654}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-158.68799}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-158.65758}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-158.65776}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-158.65778}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}    27.23
{txt}Log pseudolikelihood = {res}-158.65778{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0042
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-1.802111{col 26}{space 2} 2.961829{col 37}{space 1}   -0.61{col 46}{space 3}0.543{col 54}{space 4}-7.607189{col 67}{space 3} 4.002968
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} 2.220329{col 26}{space 2} 1.156756{col 37}{space 1}    1.92{col 46}{space 3}0.055{col 54}{space 4}-.0468704{col 67}{space 3} 4.487529
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.265397{col 26}{space 2} 1.096868{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} 1.115576{col 67}{space 3} 5.415218
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.190574{col 26}{space 2} 1.021445{col 37}{space 1}   -2.14{col 46}{space 3}0.032{col 54}{space 4} -4.19257{col 67}{space 3}-.1885779
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3208923{col 26}{space 2} .1568108{col 37}{space 1}   -2.05{col 46}{space 3}0.041{col 54}{space 4}-.6282358{col 67}{space 3}-.0135488
{txt}{space 12} {c |}
datasource~q {c |}
{space 7}OECD  {c |}{col 14}{res}{space 2}-.4026268{col 26}{space 2} 1.531275{col 37}{space 1}   -0.26{col 46}{space 3}0.793{col 54}{space 4} -3.40387{col 67}{space 3} 2.598616
{txt}{space 6}SWIID  {c |}{col 14}{res}{space 2} 1.369604{col 26}{space 2}  1.34695{col 37}{space 1}    1.02{col 46}{space 3}0.309{col 54}{space 4} -1.27037{col 67}{space 3} 4.009577
{txt}{space 8}WID  {c |}{col 14}{res}{space 2} 2.038318{col 26}{space 2} 2.299917{col 37}{space 1}    0.89{col 46}{space 3}0.375{col 54}{space 4}-2.469436{col 67}{space 3} 6.546072
{txt}{space 6}Other  {c |}{col 14}{res}{space 2}-.2265501{col 26}{space 2}  1.57721{col 37}{space 1}   -0.14{col 46}{space 3}0.886{col 54}{space 4}-3.317825{col 67}{space 3} 2.864725
{txt}{space 12} {c |}
{space 8}jipf {c |}{col 14}{res}{space 2}-.7886239{col 26}{space 2} .3920998{col 37}{space 1}   -2.01{col 46}{space 3}0.044{col 54}{space 4}-1.557125{col 67}{space 3}-.0201225
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2}  .002065{col 26}{space 2} .0031134{col 37}{space 1}    0.66{col 46}{space 3}0.507{col 54}{space 4}-.0040372{col 67}{space 3} .0081672
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1157819{col 26}{space 2} 1.521599{col 37}{space 1}   -0.08{col 46}{space 3}0.939{col 54}{space 4}-3.098061{col 67}{space 3} 2.866497
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 6.984178{col 26}{space 2} 4.011604{col 54}{space 4} 2.265679{col 67}{space 3} 21.52942
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3a
{txt}
{com}. 
. melogit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total  i.datasource_ineq c.jipf c.n_obs   , vce(robust) ||paper_id:
{res}{txt}note: {bf:2.datasource_ineq} != 0 predicts failure perfectly;
      {bf:2.datasource_ineq} omitted and 1 obs not used.

note: {bf:4.datasource_ineq} identifies no observations in the sample.

Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-90.680749}  
Iteration 1:{space 2}Log likelihood = {res: -86.88963}  
Iteration 2:{space 2}Log likelihood = {res:-86.855946}  
Iteration 3:{space 2}Log likelihood = {res:-86.855923}  
Iteration 4:{space 2}Log likelihood = {res:-86.855923}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-81.209402}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-81.209402}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-79.649244}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-79.337301}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-79.330058}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -79.33005}  
Iteration 5:{space 2}Log pseudolikelihood = {res: -79.33005}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       187
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        23

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       8.1
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}10{txt}){col 67}={res}{col 70}    58.87
{txt}Log pseudolikelihood = {res}-79.33005{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:23} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2} -6.19574{col 26}{space 2} 5.416147{col 37}{space 1}   -1.14{col 46}{space 3}0.253{col 54}{space 4}-16.81119{col 67}{space 3} 4.419714
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .6707278{col 26}{space 2} 1.410243{col 37}{space 1}    0.48{col 46}{space 3}0.634{col 54}{space 4}-2.093299{col 67}{space 3} 3.434754
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .5711265{col 26}{space 2} .6440562{col 37}{space 1}    0.89{col 46}{space 3}0.375{col 54}{space 4}-.6912005{col 67}{space 3} 1.833454
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 1.906052{col 26}{space 2}  1.52062{col 37}{space 1}    1.25{col 46}{space 3}0.210{col 54}{space 4}-1.074309{col 67}{space 3} 4.886412
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-4.591661{col 26}{space 2} 2.651992{col 37}{space 1}   -1.73{col 46}{space 3}0.083{col 54}{space 4} -9.78947{col 67}{space 3} .6061485
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2902201{col 26}{space 2} .2250312{col 37}{space 1}   -1.29{col 46}{space 3}0.197{col 54}{space 4}-.7312731{col 67}{space 3}  .150833
{txt}{space 12} {c |}
datasource~q {c |}
{space 7}OECD  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 6}SWIID  {c |}{col 14}{res}{space 2}-2.223086{col 26}{space 2} 2.414237{col 37}{space 1}   -0.92{col 46}{space 3}0.357{col 54}{space 4}-6.954904{col 67}{space 3} 2.508731
{txt}{space 8}WID  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 6}Other  {c |}{col 14}{res}{space 2}-2.321884{col 26}{space 2} 2.351613{col 37}{space 1}   -0.99{col 46}{space 3}0.323{col 54}{space 4} -6.93096{col 67}{space 3} 2.287192
{txt}{space 12} {c |}
{space 8}jipf {c |}{col 14}{res}{space 2} .0318009{col 26}{space 2} 1.122728{col 37}{space 1}    0.03{col 46}{space 3}0.977{col 54}{space 4}-2.168705{col 67}{space 3} 2.232307
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0147589{col 26}{space 2} .0094381{col 37}{space 1}    1.56{col 46}{space 3}0.118{col 54}{space 4}-.0037395{col 67}{space 3} .0332573
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1188686{col 26}{space 2} 2.355621{col 37}{space 1}   -0.05{col 46}{space 3}0.960{col 54}{space 4}-4.735801{col 67}{space 3} 4.498064
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 3.277296{col 26}{space 2} 2.504844{col 54}{space 4} .7327242{col 67}{space 3} 14.65854
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2a 
{txt}
{com}.  
. cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a m2a m3a using Table_SC8.rtf, replace label  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1 (OLS)" "M1 (Logit)" "M2 (OLS)" "M2 (Logit)" "M3 (OLS)" "M3 (Logit)") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff."  ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" datasource_ineq "Data source" controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest  *ideol_cum *party_control controls_total *datasource_ineq ) 
{res}{txt}(output written to {browse  `"Table_SC8.rtf"'})

{com}.  cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.  
. *Table S-C9: Summary information on different data sources for income inequality:
. *Stimmt nicht mit Paper überein!
. 
. tab datasource_ineqraw, sort

{txt}Data source {c |}
 inequality {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        LIS {c |}{res}        128       32.57       32.57
{txt}      SWIID {c |}{res}         99       25.19       57.76
{txt}       OECD {c |}{res}         69       17.56       75.32
{txt}        WID {c |}{res}         67       17.05       92.37
{txt}        TIS {c |}{res}         18        4.58       96.95
{txt}  Galbraith {c |}{res}          5        1.27       98.22
{txt}   Eurostat {c |}{res}          4        1.02       99.24
{txt}       WIID {c |}{res}          3        0.76      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        393      100.00
{txt}
{com}. 
. cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab using Table_SC9.rtf, replace ///
>   noobs nonumber ///
>    collabels("Obs.") ///
>   nomtitle nonote label onecell nogaps //
{res}{txt}(output written to {browse  `"Table_SC9.rtf"'})

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. * Table S-C10: Explaining partisan effects on inequality: Study-pooled effects
. 
. *Inequality measure
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. bysort paper_id ineq_meas : egen peff=mean(partyeffect)
{txt}
{com}. bysort paper_id ineq_meas  : egen goldenshare_mean=mean(goldenshare)
{txt}
{com}. bysort paper_id ineq_meas  : egen obs_mean=mean(n_obs)
{txt}
{com}. bysort paper_id ineq_meas  : gen pap_c=_N
{txt}
{com}. bysort paper_id ineq_meas  : egen obs_mean_ctr=mean(controls_total)
{txt}
{com}. 
. keep author year obs_mean goldenshare_mean ineq_meas gini_gr  toprest ideol_cum party_control obs_mean_ctr paper_id peff pap_c paper_id
{txt}
{com}. 
. duplicates drop paper_id ineq_meas , force

{p 0 4}{txt}Duplicates in terms of {res} paper_id ineq_meas{p_end}

{txt}(341 observations deleted)

{com}. d,s //63

{txt}Contains data from {res}When_Do_Parties_Affect_Economic_Inequality_Replication.dta
{txt} Observations:{res}            52                  
{txt}    Variables:{res}            13                  28 Aug 2024 14:27
{txt}Sorted by: {res}paper_id  ineq_meas
{txt}     Note: {res}Dataset has changed since last saved.
{txt}
{com}. 
. mixed peff goldenshare_mean pap_c i.ineq_meas ideol_cum party_control obs_mean_ctr obs_mean, vce(robust)  ||paper_id: 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-17.150013}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-17.145461}  
Iteration 2:{space 2}Log pseudolikelihood = {res: -17.14546}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 54}Number of obs{col 70} = {res}    52
{txt}Group variable: {res}paper_id{col 54}{txt}Number of groups{col 70} = {res}    43
{txt}{col 54}Obs per group:
{col 67}min = {res}     1
{txt}{col 67}avg = {res}   1.2
{txt}{col 67}max = {res}     3
{col 54}{txt}Wald chi2({res}8{txt}){col 70} = {res} 20.23
{txt}Log pseudolikelihood = {res} -17.14546{col 54}{txt}Prob > chi2{col 70} = {res}0.0095

{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        peff{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
goldenshar~n {c |}{col 14}{res}{space 2}-.0192503{col 26}{space 2} .3818623{col 37}{space 1}   -0.05{col 46}{space 3}0.960{col 54}{space 4}-.7676867{col 67}{space 3} .7291861
{txt}{space 7}pap_c {c |}{col 14}{res}{space 2} .0036286{col 26}{space 2}  .005073{col 37}{space 1}    0.72{col 46}{space 3}0.474{col 54}{space 4}-.0063144{col 67}{space 3} .0135715
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .0170175{col 26}{space 2} .0582542{col 37}{space 1}    0.29{col 46}{space 3}0.770{col 54}{space 4}-.0971587{col 67}{space 3} .1311937
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} .1710295{col 26}{space 2}  .131638{col 37}{space 1}    1.30{col 46}{space 3}0.194{col 54}{space 4}-.0869762{col 67}{space 3} .4290353
{txt}{space 12} {c |}
{space 3}ideol_cum {c |}{col 14}{res}{space 2} .2293265{col 26}{space 2} .1265324{col 37}{space 1}    1.81{col 46}{space 3}0.070{col 54}{space 4}-.0186725{col 67}{space 3} .4773255
{txt}party_cont~l {c |}{col 14}{res}{space 2} -.196683{col 26}{space 2} .1153839{col 37}{space 1}   -1.70{col 46}{space 3}0.088{col 54}{space 4}-.4228314{col 67}{space 3} .0294654
{txt}obs_mean_ctr {c |}{col 14}{res}{space 2}-.0246822{col 26}{space 2} .0395318{col 37}{space 1}   -0.62{col 46}{space 3}0.532{col 54}{space 4}-.1021631{col 67}{space 3} .0527987
{txt}{space 4}obs_mean {c |}{col 14}{res}{space 2} .0002481{col 26}{space 2} .0003453{col 37}{space 1}    0.72{col 46}{space 3}0.472{col 54}{space 4}-.0004287{col 67}{space 3} .0009249
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .369968{col 26}{space 2} .2343114{col 37}{space 1}    1.58{col 46}{space 3}0.114{col 54}{space 4}-.0892739{col 67}{space 3} .8292099
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}paper_id{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .1058635{col 44} .0363993{col 58} .0539604{col 70} .2076908
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0289187{col 44}  .022633{col 58} .0062372{col 70} .1340807
{txt}{hline 29}{c BT}{hline 48}

{com}. est store m1
{txt}
{com}. 
. *Top vs. rest
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. bysort paper_id toprest : egen peff=mean(partyeffect)
{txt}
{com}. bysort paper_id toprest  : egen goldenshare_mean=mean(goldenshare)
{txt}
{com}. bysort paper_id toprest  : egen obs_mean=mean(n_obs)
{txt}
{com}. bysort paper_id toprest  : gen pap_c=_N
{txt}
{com}. bysort paper_id toprest  : egen obs_mean_ctr=mean(controls_total)
{txt}
{com}. 
. duplicates drop paper_id toprest , force

{p 0 4}{txt}Duplicates in terms of {res} paper_id toprest{p_end}

{txt}(341 observations deleted)

{com}. d,s //63

{txt}Contains data from {res}When_Do_Parties_Affect_Economic_Inequality_Replication.dta
{txt} Observations:{res}            52                  
{txt}    Variables:{res}            38                  28 Aug 2024 14:27
{txt}Sorted by: {res}paper_id  toprest
{txt}     Note: {res}Dataset has changed since last saved.
{txt}
{com}. 
. mixed peff goldenshare_mean  pap_c 1.toprest ideol_cum party_control obs_mean_ctr obs_mean, vce(robust)  ||paper_id: 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-20.774572}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-20.068133}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-20.067106}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-20.067106}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 54}Number of obs{col 70} = {res}    52
{txt}Group variable: {res}paper_id{col 54}{txt}Number of groups{col 70} = {res}    43
{txt}{col 54}Obs per group:
{col 67}min = {res}     1
{txt}{col 67}avg = {res}   1.2
{txt}{col 67}max = {res}     2
{col 54}{txt}Wald chi2({res}7{txt}){col 70} = {res} 45.10
{txt}Log pseudolikelihood = {res}-20.067106{col 54}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        peff{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
goldenshar~n {c |}{col 14}{res}{space 2} .0925294{col 26}{space 2} .3589266{col 37}{space 1}    0.26{col 46}{space 3}0.797{col 54}{space 4}-.6109539{col 67}{space 3} .7960126
{txt}{space 7}pap_c {c |}{col 14}{res}{space 2}-.0000932{col 26}{space 2}   .00635{col 37}{space 1}   -0.01{col 46}{space 3}0.988{col 54}{space 4}-.0125389{col 67}{space 3} .0123525
{txt}{space 3}1.toprest {c |}{col 14}{res}{space 2}  .185605{col 26}{space 2} .1091475{col 37}{space 1}    1.70{col 46}{space 3}0.089{col 54}{space 4}-.0283202{col 67}{space 3} .3995303
{txt}{space 3}ideol_cum {c |}{col 14}{res}{space 2} .2675929{col 26}{space 2} .1089515{col 37}{space 1}    2.46{col 46}{space 3}0.014{col 54}{space 4} .0540519{col 67}{space 3} .4811338
{txt}party_cont~l {c |}{col 14}{res}{space 2}-.2434523{col 26}{space 2} .1255029{col 37}{space 1}   -1.94{col 46}{space 3}0.052{col 54}{space 4}-.4894335{col 67}{space 3} .0025289
{txt}obs_mean_ctr {c |}{col 14}{res}{space 2}-.0317527{col 26}{space 2} .0348713{col 37}{space 1}   -0.91{col 46}{space 3}0.363{col 54}{space 4}-.1000992{col 67}{space 3} .0365937
{txt}{space 4}obs_mean {c |}{col 14}{res}{space 2} .0004316{col 26}{space 2} .0002903{col 37}{space 1}    1.49{col 46}{space 3}0.137{col 54}{space 4}-.0001375{col 67}{space 3} .0010007
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3271968{col 26}{space 2} .2178701{col 37}{space 1}    1.50{col 46}{space 3}0.133{col 54}{space 4}-.0998208{col 67}{space 3} .7542144
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}paper_id{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} 3.69e-10{col 44} 2.20e-09{col 58} 3.16e-15{col 70} .0000432
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .1266833{col 44} .0212812{col 58} .0911439{col 70} .1760803
{txt}{hline 29}{c BT}{hline 48}

{com}. est store m3
{txt}
{com}. 
. *Gini types
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. bysort paper_id gini_gr : egen peff=mean(partyeffect)
{txt}
{com}. bysort paper_id gini_gr  : egen goldenshare_mean=mean(goldenshare)
{txt}
{com}. bysort paper_id gini_gr  : egen obs_mean=mean(n_obs)
{txt}
{com}. bysort paper_id gini_gr  : gen pap_c=_N
{txt}
{com}. bysort paper_id gini_gr  : egen obs_mean_ctr=mean(controls_total)
{txt}
{com}. 
. duplicates drop paper_id gini_gr , force

{p 0 4}{txt}Duplicates in terms of {res} paper_id gini_gr{p_end}

{txt}(333 observations deleted)

{com}. d,s //63

{txt}Contains data from {res}When_Do_Parties_Affect_Economic_Inequality_Replication.dta
{txt} Observations:{res}            60                  
{txt}    Variables:{res}            38                  28 Aug 2024 14:27
{txt}Sorted by: {res}paper_id  gini_gr
{txt}     Note: {res}Dataset has changed since last saved.
{txt}
{com}. 
. mixed peff goldenshare_mean pap_c ideol_cum party_control obs_mean i.gini_gr obs_mean_ctr, vce(robust)  ||paper_id: 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-9.8863077}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-9.8757958}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-9.8757008}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-9.8757008}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 54}Number of obs{col 70} = {res}    35
{txt}Group variable: {res}paper_id{col 54}{txt}Number of groups{col 70} = {res}    24
{txt}{col 54}Obs per group:
{col 67}min = {res}     1
{txt}{col 67}avg = {res}   1.5
{txt}{col 67}max = {res}     3
{col 54}{txt}Wald chi2({res}8{txt}){col 70} = {res} 42.49
{txt}Log pseudolikelihood = {res}-9.8757008{col 54}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        peff{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
goldenshar~n {c |}{col 14}{res}{space 2}-.3672145{col 26}{space 2} .4676527{col 37}{space 1}   -0.79{col 46}{space 3}0.432{col 54}{space 4}-1.283797{col 67}{space 3} .5493678
{txt}{space 7}pap_c {c |}{col 14}{res}{space 2}-.0032657{col 26}{space 2} .0076307{col 37}{space 1}   -0.43{col 46}{space 3}0.669{col 54}{space 4}-.0182215{col 67}{space 3} .0116902
{txt}{space 3}ideol_cum {c |}{col 14}{res}{space 2} .1963722{col 26}{space 2} .1523205{col 37}{space 1}    1.29{col 46}{space 3}0.197{col 54}{space 4}-.1021705{col 67}{space 3} .4949148
{txt}party_cont~l {c |}{col 14}{res}{space 2}-.3819268{col 26}{space 2} .1793811{col 37}{space 1}   -2.13{col 46}{space 3}0.033{col 54}{space 4}-.7335072{col 67}{space 3}-.0303464
{txt}{space 4}obs_mean {c |}{col 14}{res}{space 2} .0010648{col 26}{space 2} .0004996{col 37}{space 1}    2.13{col 46}{space 3}0.033{col 54}{space 4} .0000856{col 67}{space 3} .0020439
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2}  .200734{col 26}{space 2} .1234438{col 37}{space 1}    1.63{col 46}{space 3}0.104{col 54}{space 4}-.0412113{col 67}{space 3} .4426793
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} .1538592{col 26}{space 2} .0849791{col 37}{space 1}    1.81{col 46}{space 3}0.070{col 54}{space 4}-.0126967{col 67}{space 3} .3204151
{txt}{space 12} {c |}
obs_mean_ctr {c |}{col 14}{res}{space 2}-.1023927{col 26}{space 2} .0308698{col 37}{space 1}   -3.32{col 46}{space 3}0.001{col 54}{space 4}-.1628964{col 67}{space 3}-.0418889
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6743279{col 26}{space 2} .2030193{col 37}{space 1}    3.32{col 46}{space 3}0.001{col 54}{space 4} .2764174{col 67}{space 3} 1.072239
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}paper_id{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .0795058{col 44} .0539353{col 58} .0210358{col 70} .3004957
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} .0436293{col 44}  .037723{col 58} .0080132{col 70} .2375466
{txt}{hline 29}{c BT}{hline 48}

{com}. est store m2
{txt}
{com}. 
. cd  "$RESULTSDIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1 m2 m3 using Table_SC10.rtf, label replace compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1 (OLS)" "M1 (Logit)" "M2 (OLS)" "M2 (Logit)" "M3 (OLS)" "M3 (Logit)") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff."  ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare_mean "% of Golden age" ///
>  pap_c "Models/paper" ///
>  obs_mean "N of observations"  _cons "Constant" obs_mean_ctr "Total N of policy channels") ///
>  order( *goldenshare_mean *ineq_meas *gini_gr *toprest *ideol_cum *party_control controls_total)
{res}{txt}(output written to {browse  `"Table_SC10.rtf"'})

{com}. cd  "$DATADIR"
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. 
. *Table S-C11: Explaining partisan effects on inequality: Inequality vs. redistribution
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. tab ineqred ineq_meas

{txt}Inequality (1) {c |}
            or {c |}
redistribution {c |}        Inequality measure
           (2) {c |}      Gini      Ratio      Share {c |}     Total
{hline 15}{c +}{hline 33}{c +}{hline 10}
    Inequality {c |}{res}       159        109         82 {txt}{c |}{res}       350 
{txt}Redistribution {c |}{res}        29          1         13 {txt}{c |}{res}        43 
{txt}{hline 15}{c +}{hline 33}{c +}{hline 10}
         Total {c |}{res}       188        110         95 {txt}{c |}{res}       393 
{txt}
{com}. 
. melogit partyeffect c.goldenshare ib0.party_control i.ineq_meas   ib0.ideol_cum  controls_total  c.jipf c.n_obs   i.ineqred, ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res: -213.8109}  
Iteration 1:{space 2}Log likelihood = {res:-213.12352}  
Iteration 2:{space 2}Log likelihood = {res:-213.12222}  
Iteration 3:{space 2}Log likelihood = {res:-213.12222}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-177.77162}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log likelihood = {res:-177.77162}  
Iteration 1:{space 2}Log likelihood = {res:-169.55133}  
Iteration 2:{space 2}Log likelihood = {res:-167.53534}  
Iteration 3:{space 2}Log likelihood = {res: -167.2731}  
Iteration 4:{space 2}Log likelihood = {res:-167.26974}  
Iteration 5:{space 2}Log likelihood = {res:-167.27003}  
Iteration 6:{space 2}Log likelihood = {res:-167.27005}  
Iteration 7:{space 2}Log likelihood = {res:-167.27005}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}    15.24
{txt}Log likelihood = {res}-167.27005{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0847
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-3.532524{col 26}{space 2} 3.583478{col 37}{space 1}   -0.99{col 46}{space 3}0.324{col 54}{space 4}-10.55601{col 67}{space 3} 3.490963
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.920505{col 26}{space 2} 1.208396{col 37}{space 1}   -1.59{col 46}{space 3}0.112{col 54}{space 4}-4.288917{col 67}{space 3} .4479068
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .5011129{col 26}{space 2} .6363476{col 37}{space 1}    0.79{col 46}{space 3}0.431{col 54}{space 4}-.7461055{col 67}{space 3} 1.748331
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.354064{col 26}{space 2} .9309828{col 37}{space 1}    2.53{col 46}{space 3}0.011{col 54}{space 4}  .529371{col 67}{space 3} 4.178757
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2}  3.20861{col 26}{space 2} 1.156417{col 37}{space 1}    2.77{col 46}{space 3}0.006{col 54}{space 4} .9420741{col 67}{space 3} 5.475145
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2523549{col 26}{space 2} .1748452{col 37}{space 1}   -1.44{col 46}{space 3}0.149{col 54}{space 4}-.5950452{col 67}{space 3} .0903353
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.7234939{col 26}{space 2} .4059422{col 37}{space 1}   -1.78{col 46}{space 3}0.075{col 54}{space 4}-1.519126{col 67}{space 3} .0721382
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0043241{col 26}{space 2} .0026783{col 37}{space 1}    1.61{col 46}{space 3}0.106{col 54}{space 4}-.0009253{col 67}{space 3} .0095735
{txt}{space 12} {c |}
{space 5}ineqred {c |}
Redistrib~n  {c |}{col 14}{res}{space 2} .4791193{col 26}{space 2} 1.045027{col 37}{space 1}    0.46{col 46}{space 3}0.647{col 54}{space 4}-1.569097{col 67}{space 3} 2.527335
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2007325{col 26}{space 2} 1.401608{col 37}{space 1}   -0.14{col 46}{space 3}0.886{col 54}{space 4}-2.947834{col 67}{space 3}  2.54637
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2}  7.20322{col 26}{space 2} 3.472028{col 54}{space 4} 2.800519{col 67}{space 3} 18.52741
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 91.70{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000
{txt}
{com}.  est store m1a
{txt}
{com}. 
. melogit partyeffect c.goldenshare toprest  ib0.ideol_cum ib0.party_control controls_total c.jipf c.n_obs  i.ineqred, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res: -209.2686}  
Iteration 1:{space 2}Log likelihood = {res:-208.67073}  
Iteration 2:{space 2}Log likelihood = {res:-208.66978}  
Iteration 3:{space 2}Log likelihood = {res:-208.66978}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-172.97512}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-172.97512}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-163.87185}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-160.78734}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-159.95015}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -159.8823}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-159.88298}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-159.88309}  
Iteration 7:{space 2}Log pseudolikelihood = {res: -159.8831}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    18.59
{txt}Log pseudolikelihood = {res}-159.8831{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0172
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.465383{col 26}{space 2}  2.89002{col 37}{space 1}   -0.85{col 46}{space 3}0.394{col 54}{space 4}-8.129719{col 67}{space 3} 3.198952
{txt}{space 5}toprest {c |}{col 14}{res}{space 2}  2.37698{col 26}{space 2} 1.084286{col 37}{space 1}    2.19{col 46}{space 3}0.028{col 54}{space 4} .2518178{col 67}{space 3} 4.502141
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.249035{col 26}{space 2} 1.063949{col 37}{space 1}    3.05{col 46}{space 3}0.002{col 54}{space 4} 1.163734{col 67}{space 3} 5.334336
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.845544{col 26}{space 2} 1.083147{col 37}{space 1}   -1.70{col 46}{space 3}0.088{col 54}{space 4}-3.968472{col 67}{space 3} .2773852
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3161096{col 26}{space 2}  .160239{col 37}{space 1}   -1.97{col 46}{space 3}0.049{col 54}{space 4}-.6301723{col 67}{space 3}-.0020468
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.7339704{col 26}{space 2} .3668064{col 37}{space 1}   -2.00{col 46}{space 3}0.045{col 54}{space 4}-1.452898{col 67}{space 3} -.015043
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0047887{col 26}{space 2} .0025111{col 37}{space 1}    1.91{col 46}{space 3}0.057{col 54}{space 4}-.0001329{col 67}{space 3} .0097103
{txt}{space 12} {c |}
{space 5}ineqred {c |}
Redistrib~n  {c |}{col 14}{res}{space 2} .7812987{col 26}{space 2} .6703816{col 37}{space 1}    1.17{col 46}{space 3}0.244{col 54}{space 4} -.532625{col 67}{space 3} 2.095222
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6089301{col 26}{space 2} 1.483144{col 37}{space 1}   -0.41{col 46}{space 3}0.681{col 54}{space 4}-3.515838{col 67}{space 3} 2.297978
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.072615{col 26}{space 2} 4.467674{col 54}{space 4}  2.72853{col 67}{space 3} 23.88359
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3a
{txt}
{com}. 
. melogit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs i.ineqred, vce(robust) ||paper_id:
{res}{txt}note: {bf:2.ineqred} omitted because of collinearity.

Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-90.987765}  
Iteration 1:{space 2}Log likelihood = {res:-88.248259}  
Iteration 2:{space 2}Log likelihood = {res:-88.219698}  
Iteration 3:{space 2}Log likelihood = {res:-88.219697}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-81.648807}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-81.648807}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-80.188917}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-79.955931}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-79.946748}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-79.946731}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-79.946731}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       188
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.8
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    39.48
{txt}Log pseudolikelihood = {res}-79.946731{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-4.550399{col 26}{space 2} 4.236364{col 37}{space 1}   -1.07{col 46}{space 3}0.283{col 54}{space 4}-12.85352{col 67}{space 3} 3.752722
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .7103867{col 26}{space 2} 1.269799{col 37}{space 1}    0.56{col 46}{space 3}0.576{col 54}{space 4}-1.778374{col 67}{space 3} 3.199147
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} 1.000453{col 26}{space 2} .7109585{col 37}{space 1}    1.41{col 46}{space 3}0.159{col 54}{space 4}-.3930002{col 67}{space 3} 2.393906
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.065932{col 26}{space 2} 1.462413{col 37}{space 1}    1.41{col 46}{space 3}0.158{col 54}{space 4}-.8003457{col 67}{space 3} 4.932209
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-3.612916{col 26}{space 2} 1.872056{col 37}{space 1}   -1.93{col 46}{space 3}0.054{col 54}{space 4}-7.282078{col 67}{space 3}  .056246
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3193909{col 26}{space 2} .2077367{col 37}{space 1}   -1.54{col 46}{space 3}0.124{col 54}{space 4}-.7265473{col 67}{space 3} .0877654
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.3999904{col 26}{space 2}  .860415{col 37}{space 1}   -0.46{col 46}{space 3}0.642{col 54}{space 4}-2.086373{col 67}{space 3} 1.286392
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0084538{col 26}{space 2}  .004761{col 37}{space 1}    1.78{col 46}{space 3}0.076{col 54}{space 4}-.0008775{col 67}{space 3} .0177851
{txt}{space 12} {c |}
{space 5}ineqred {c |}
Redistrib~n  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} .1457716{col 26}{space 2} 2.072666{col 37}{space 1}    0.07{col 46}{space 3}0.944{col 54}{space 4} -3.91658{col 67}{space 3} 4.208123
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 3.139942{col 26}{space 2} 2.371739{col 54}{space 4} .7144483{col 67}{space 3} 13.79979
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2a 
{txt}
{com}.  
. 
. melogit partyeffect c.goldenshare ib0.party_control i.ineq_meas   ib0.ideol_cum  controls_total  c.jipf c.n_obs   if ineqred==1, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-183.99857}  
Iteration 1:{space 2}Log likelihood = {res: -183.4403}  
Iteration 2:{space 2}Log likelihood = {res:-183.43893}  
Iteration 3:{space 2}Log likelihood = {res:-183.43893}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-154.82259}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-154.82259}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-147.47145}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.51459}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.10908}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -145.0939}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-145.09427}  
Iteration 6:{space 2}Log pseudolikelihood = {res: -145.0943}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-145.09431}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       350
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        37

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.5
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    28.33
{txt}Log pseudolikelihood = {res}-145.09431{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0004
{txt}{ralign 78:(Std. err. adjusted for {res:37} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-3.272879{col 26}{space 2} 3.323155{col 37}{space 1}   -0.98{col 46}{space 3}0.325{col 54}{space 4}-9.786143{col 67}{space 3} 3.240384
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.489317{col 26}{space 2} 1.101299{col 37}{space 1}   -1.35{col 46}{space 3}0.176{col 54}{space 4}-3.647824{col 67}{space 3} .6691897
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .5746635{col 26}{space 2}  .329604{col 37}{space 1}    1.74{col 46}{space 3}0.081{col 54}{space 4}-.0713485{col 67}{space 3} 1.220676
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.757785{col 26}{space 2} .8756924{col 37}{space 1}    3.15{col 46}{space 3}0.002{col 54}{space 4}  1.04146{col 67}{space 3} 4.474111
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.581766{col 26}{space 2}  1.10189{col 37}{space 1}    2.34{col 46}{space 3}0.019{col 54}{space 4} .4221002{col 67}{space 3} 4.741432
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.4309022{col 26}{space 2} .1519712{col 37}{space 1}   -2.84{col 46}{space 3}0.005{col 54}{space 4}-.7287603{col 67}{space 3} -.133044
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.8257702{col 26}{space 2} .5339383{col 37}{space 1}   -1.55{col 46}{space 3}0.122{col 54}{space 4} -1.87227{col 67}{space 3} .2207296
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0038104{col 26}{space 2} .0028194{col 37}{space 1}    1.35{col 46}{space 3}0.177{col 54}{space 4}-.0017154{col 67}{space 3} .0093363
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .9241029{col 26}{space 2} 1.511208{col 37}{space 1}    0.61{col 46}{space 3}0.541{col 54}{space 4} -2.03781{col 67}{space 3} 3.886016
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 7.217686{col 26}{space 2} 3.868237{col 54}{space 4} 2.524679{col 67}{space 3}  20.6343
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1b
{txt}
{com}. 
. melogit partyeffect c.goldenshare toprest  ib0.ideol_cum ib0.party_control controls_total c.jipf c.n_obs   if ineqred==1, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-182.90443}  
Iteration 1:{space 2}Log likelihood = {res:-182.53394}  
Iteration 2:{space 2}Log likelihood = {res:-182.53296}  
Iteration 3:{space 2}Log likelihood = {res:-182.53296}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-151.78222}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-151.78222}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-142.82475}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-139.09884}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-138.65716}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-138.63737}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-138.63789}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-138.63792}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-138.63792}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       350
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        37

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.5
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    14.34
{txt}Log pseudolikelihood = {res}-138.63792{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0454
{txt}{ralign 78:(Std. err. adjusted for {res:37} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.878275{col 26}{space 2} 3.493405{col 37}{space 1}   -0.82{col 46}{space 3}0.410{col 54}{space 4}-9.725224{col 67}{space 3} 3.968674
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} 2.436294{col 26}{space 2} 1.145749{col 37}{space 1}    2.13{col 46}{space 3}0.033{col 54}{space 4} .1906663{col 67}{space 3} 4.681922
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.750902{col 26}{space 2} 1.187438{col 37}{space 1}    2.32{col 46}{space 3}0.021{col 54}{space 4} .4235653{col 67}{space 3} 5.078238
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.493059{col 26}{space 2} 1.205419{col 37}{space 1}   -1.24{col 46}{space 3}0.215{col 54}{space 4}-3.855638{col 67}{space 3} .8695196
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.5051614{col 26}{space 2} .1815807{col 37}{space 1}   -2.78{col 46}{space 3}0.005{col 54}{space 4} -.861053{col 67}{space 3}-.1492697
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.7751186{col 26}{space 2} .5380555{col 37}{space 1}   -1.44{col 46}{space 3}0.150{col 54}{space 4}-1.829688{col 67}{space 3} .2794508
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0046961{col 26}{space 2} .0029141{col 37}{space 1}    1.61{col 46}{space 3}0.107{col 54}{space 4}-.0010154{col 67}{space 3} .0104076
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4788692{col 26}{space 2} 1.645791{col 37}{space 1}    0.29{col 46}{space 3}0.771{col 54}{space 4}-2.746823{col 67}{space 3} 3.704561
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 9.026717{col 26}{space 2} 5.563098{col 54}{space 4} 2.697372{col 67}{space 3} 30.20779
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3b
{txt}
{com}. 
. melogit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs  if ineqred==1 , vce(robust) ||paper_id: 
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-70.587109}  
Iteration 1:{space 2}Log likelihood = {res:-67.787831}  
Iteration 2:{space 2}Log likelihood = {res:-67.766123}  
Iteration 3:{space 2}Log likelihood = {res:-67.766115}  
Iteration 4:{space 2}Log likelihood = {res:-67.766115}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-65.372959}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-65.372959}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-64.321529}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-64.207918}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-64.205069}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-64.205062}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-64.205062}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       159
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        20

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       8.0
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    17.44
{txt}Log pseudolikelihood = {res}-64.205062{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0148
{txt}{ralign 78:(Std. err. adjusted for {res:20} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-7.130527{col 26}{space 2} 4.814399{col 37}{space 1}   -1.48{col 46}{space 3}0.139{col 54}{space 4}-16.56658{col 67}{space 3} 2.305522
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .7427952{col 26}{space 2} 1.277548{col 37}{space 1}    0.58{col 46}{space 3}0.561{col 54}{space 4}-1.761153{col 67}{space 3} 3.246743
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2}  2.19776{col 26}{space 2} 1.733072{col 37}{space 1}    1.27{col 46}{space 3}0.205{col 54}{space 4}-1.198999{col 67}{space 3}  5.59452
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-3.192206{col 26}{space 2} 2.225372{col 37}{space 1}   -1.43{col 46}{space 3}0.151{col 54}{space 4}-7.553856{col 67}{space 3} 1.169443
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.4857255{col 26}{space 2} .2710786{col 37}{space 1}   -1.79{col 46}{space 3}0.073{col 54}{space 4} -1.01703{col 67}{space 3} .0455789
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-1.132275{col 26}{space 2} 1.079721{col 37}{space 1}   -1.05{col 46}{space 3}0.294{col 54}{space 4}-3.248489{col 67}{space 3} .9839398
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0096428{col 26}{space 2} .0055768{col 37}{space 1}    1.73{col 46}{space 3}0.084{col 54}{space 4}-.0012875{col 67}{space 3} .0205732
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.857912{col 26}{space 2} 2.173552{col 37}{space 1}    0.85{col 46}{space 3}0.393{col 54}{space 4}-2.402172{col 67}{space 3} 6.117995
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 2.575012{col 26}{space 2} 2.580742{col 54}{space 4} .3611467{col 67}{space 3} 18.36009
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2b
{txt}
{com}.  
. melogit partyeffect c.goldenshare ib0.party_control i.ineq_meas   ib0.ideol_cum  controls_total  c.jipf c.n_obs   if ineqred==2, vce(robust) ||paper_id:
{res}{txt}note: {bf:0.party_control} != 1 predicts failure perfectly;
      {bf:0.party_control} omitted and 2 obs not used.

note: {bf:2.ineq_meas} != 0 predicts failure perfectly;
      {bf:2.ineq_meas} omitted and 1 obs not used.

note: {bf:1.party_control} omitted because of collinearity.

Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-18.056412}  
Iteration 1:{space 2}Log likelihood = {res:-16.926759}  
Iteration 2:{space 2}Log likelihood = {res:-15.334231}  
Iteration 3:{space 2}Log likelihood = {res:-13.699743}  
Iteration 4:{space 2}Log likelihood = {res:-13.613882}  
Iteration 5:{space 2}Log likelihood = {res:-13.608483}  
Iteration 6:{space 2}Log likelihood = {res:-13.608464}  
Iteration 7:{space 2}Log likelihood = {res:-13.608464}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-14.773265}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-14.773265}  (not concave)
Iteration 1:{space 2}Log pseudolikelihood = {res: -14.04125}  (not concave)
Iteration 2:{space 2}Log pseudolikelihood = {res:-13.728816}  (not concave)
Iteration 3:{space 2}Log pseudolikelihood = {res:-13.666184}  (not concave)
Iteration 4:{space 2}Log pseudolikelihood = {res:-13.615241}  (not concave)
Iteration 5:{space 2}Log pseudolikelihood = {res:-13.612701}  (not concave)
Iteration 6:{space 2}Log pseudolikelihood = {res:-13.611685}  (not concave)
Iteration 7:{space 2}Log pseudolikelihood = {res:-13.611583}  (not concave)
Iteration 8:{space 2}Log pseudolikelihood = {res:-13.611542}  (not concave)
Iteration 9:{space 2}Log pseudolikelihood = {res: -13.61151}  (not concave)
Iteration 10:{space 1}Log pseudolikelihood = {res:-13.611507}  (not concave)
Iteration 11:{space 1}Log pseudolikelihood = {res:-13.611505}  (not concave)
Iteration 12:{space 1}Log pseudolikelihood = {res:-13.611505}  (not concave)
Iteration 13:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 14:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 15:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 16:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 17:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 18:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 19:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 20:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 21:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 22:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 23:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 24:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 25:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 26:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 27:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 28:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 29:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 30:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 31:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 32:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 33:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 34:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 35:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 36:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 37:{space 1}Log pseudolikelihood = {res:-13.611504}  (not concave)
Iteration 38:{space 1}Log pseudolikelihood = {res:-13.611504}  
Iteration 39:{space 1}Log pseudolikelihood = {res:-13.611504}  (backed up)
Iteration 40:{space 1}Log pseudolikelihood = {res:-13.611502}  (backed up)
Iteration 41:{space 1}Log pseudolikelihood = {res:-13.611501}  (not concave)
Iteration 42:{space 1}Log pseudolikelihood = {res:-13.611501}  (not concave)
Iteration 43:{space 1}Log pseudolikelihood = {res:  -13.6115}  
Iteration 44:{space 1}Log pseudolikelihood = {res:-13.611406}  (not concave)
Iteration 45:{space 1}Log pseudolikelihood = {res:-13.611401}  
Iteration 46:{space 1}Log pseudolikelihood = {res:-13.611221}  (not concave)
Iteration 47:{space 1}Log pseudolikelihood = {res:-13.611221}  (backed up)
Iteration 48:{space 1}Log pseudolikelihood = {res:-13.610578}  
Iteration 49:{space 1}Log pseudolikelihood = {res:-13.608464}  (not concave)
Iteration 50:{space 1}Log pseudolikelihood = {res:-13.608464}  (backed up)
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}        40
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        10

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       4.0
{col 63}{txt}max{col 67}={res}{col 69}        10

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}6{txt}){col 67}={res}{col 70} 2.10e+08
{txt}Log pseudolikelihood = {res}-13.608464{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:10} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2} .9442141{col 26}{space 2} 12.64594{col 37}{space 1}    0.07{col 46}{space 3}0.940{col 54}{space 4}-23.84138{col 67}{space 3} 25.72981
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
{space 6}Share  {c |}{col 14}{res}{space 2}-46.88351{col 26}{space 2} 7.039115{col 37}{space 1}   -6.66{col 46}{space 3}0.000{col 54}{space 4}-60.67992{col 67}{space 3} -33.0871
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.884895{col 26}{space 2}  4.00948{col 37}{space 1}    0.97{col 46}{space 3}0.333{col 54}{space 4}-3.973541{col 67}{space 3} 11.74333
{txt}controls_t~l {c |}{col 14}{res}{space 2} .7660617{col 26}{space 2} 1.452652{col 37}{space 1}    0.53{col 46}{space 3}0.598{col 54}{space 4}-2.081084{col 67}{space 3} 3.613207
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} 8.501426{col 26}{space 2} 1.570582{col 37}{space 1}    5.41{col 46}{space 3}0.000{col 54}{space 4} 5.423141{col 67}{space 3} 11.57971
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0143071{col 26}{space 2}  .010436{col 37}{space 1}    1.37{col 46}{space 3}0.170{col 54}{space 4} -.006147{col 67}{space 3} .0347613
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-21.39245{col 26}{space 2} 6.188914{col 37}{space 1}   -3.46{col 46}{space 3}0.001{col 54}{space 4} -33.5225{col 67}{space 3}-9.262399
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.53e-34{col 26}{space 2} 1.29e-32{col 54}{space 4} 1.02e-46{col 67}{space 3} 7.11e-21
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1c
{txt}
{com}. 
. 
.  cd  "$RESULTSDIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a  m2a m3a m1b m2b m3b m1c using Table_SC11_ineqred.rtf, label replace  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1" "M2" "M3" "M4" "M5" "M6" "M7") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff." ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
>  controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest  *ideol_cum *party_control *controls_total)
{res}{txt}(output written to {browse  `"Table_SC11_ineqred.rtf"'})

{com}. cd  "$DATADIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *Table S-C12: Explaining partisan effects on inequality: Controlling for measures before/after taxes and transfers 
. tab prepost ineq_meas

             {txt}{c |}        Inequality measure
Pre/post tax {c |}      Gini      Ratio      Share {c |}     Total
{hline 13}{c +}{hline 33}{c +}{hline 10}
     pre-tax {c |}{res}        65         85         82 {txt}{c |}{res}       232 
{txt}    post-tax {c |}{res}        94         24          0 {txt}{c |}{res}       118 
{txt}pre vs. post {c |}{res}        29          1         13 {txt}{c |}{res}        43 
{txt}{hline 13}{c +}{hline 33}{c +}{hline 10}
       Total {c |}{res}       188        110         95 {txt}{c |}{res}       393 
{txt}
{com}. 
. gen gini=0
{txt}
{com}. replace gini=1 if gini_gr!=.
{txt}(188 real changes made)

{com}. tab gini

       {txt}gini {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        205       52.16       52.16
{txt}          1 {c |}{res}        188       47.84      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        393      100.00
{txt}
{com}. 
. melogit partyeffect c.goldenshare ib0.party_control ib1.prepost i.ineq_meas i.prepost  ib0.ideol_cum  controls_total  c.jipf c.n_obs  if ineqred==1 , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-181.02674}  
Iteration 1:{space 2}Log likelihood = {res:-180.18948}  
Iteration 2:{space 2}Log likelihood = {res:-180.18837}  
Iteration 3:{space 2}Log likelihood = {res:-180.18837}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-153.94314}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-153.94314}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-147.04391}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-145.33844}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-145.05905}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-145.05045}  
Iteration 5:{space 2}Log pseudolikelihood = {res: -145.0507}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-145.05072}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-145.05073}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       350
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        37

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.5
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}    31.08
{txt}Log pseudolikelihood = {res}-145.05073{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0003
{txt}{ralign 78:(Std. err. adjusted for {res:37} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-3.248346{col 26}{space 2} 3.289782{col 37}{space 1}   -0.99{col 46}{space 3}0.323{col 54}{space 4}-9.696201{col 67}{space 3} 3.199509
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.499953{col 26}{space 2} 1.108017{col 37}{space 1}   -1.35{col 46}{space 3}0.176{col 54}{space 4}-3.671627{col 67}{space 3} .6717211
{txt}{space 12} {c |}
{space 5}prepost {c |}
{space 3}post-tax  {c |}{col 14}{res}{space 2}  .202489{col 26}{space 2} 1.008113{col 37}{space 1}    0.20{col 46}{space 3}0.841{col 54}{space 4}-1.773376{col 67}{space 3} 2.178354
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .5816988{col 26}{space 2} .3153014{col 37}{space 1}    1.84{col 46}{space 3}0.065{col 54}{space 4}-.0362805{col 67}{space 3} 1.199678
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.777601{col 26}{space 2} .8725635{col 37}{space 1}    3.18{col 46}{space 3}0.001{col 54}{space 4} 1.067408{col 67}{space 3} 4.487794
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.525443{col 26}{space 2} 1.163218{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .2455783{col 67}{space 3} 4.805308
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.4293575{col 26}{space 2} .1500185{col 37}{space 1}   -2.86{col 46}{space 3}0.004{col 54}{space 4}-.7233883{col 67}{space 3}-.1353267
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.8095093{col 26}{space 2}  .545707{col 37}{space 1}   -1.48{col 46}{space 3}0.138{col 54}{space 4}-1.879075{col 67}{space 3} .2600568
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0037946{col 26}{space 2} .0028032{col 37}{space 1}    1.35{col 46}{space 3}0.176{col 54}{space 4}-.0016995{col 67}{space 3} .0092887
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .8580242{col 26}{space 2} 1.506865{col 37}{space 1}    0.57{col 46}{space 3}0.569{col 54}{space 4}-2.095378{col 67}{space 3} 3.811426
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2}  7.00032{col 26}{space 2} 3.762661{col 54}{space 4} 2.441173{col 67}{space 3} 20.07416
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1a
{txt}
{com}. 
. melogit partyeffect c.goldenshare 1.toprest ib1.prepost  ib0.ideol_cum ib0.party_control controls_total c.jipf c.n_obs if ineqred==1, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res: -178.9249}  
Iteration 1:{space 2}Log likelihood = {res:-178.30467}  
Iteration 2:{space 2}Log likelihood = {res:-178.30422}  
Iteration 3:{space 2}Log likelihood = {res:-178.30422}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-150.27841}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-150.27841}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-141.80039}  
Iteration 2:{space 2}Log pseudolikelihood = {res: -139.3184}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-138.66438}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-138.62875}  
Iteration 5:{space 2}Log pseudolikelihood = {res: -138.6296}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-138.62966}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-138.62966}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       350
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        37

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.5
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    15.12
{txt}Log pseudolikelihood = {res}-138.62966{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0569
{txt}{ralign 78:(Std. err. adjusted for {res:37} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.858343{col 26}{space 2} 3.428071{col 37}{space 1}   -0.83{col 46}{space 3}0.404{col 54}{space 4}-9.577238{col 67}{space 3} 3.860552
{txt}{space 3}1.toprest {c |}{col 14}{res}{space 2} 2.436474{col 26}{space 2} 1.150677{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} .1811889{col 67}{space 3} 4.691759
{txt}{space 12} {c |}
{space 5}prepost {c |}
{space 3}post-tax  {c |}{col 14}{res}{space 2} .0939517{col 26}{space 2} 1.133826{col 37}{space 1}    0.08{col 46}{space 3}0.934{col 54}{space 4}-2.128306{col 67}{space 3} 2.316209
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.718896{col 26}{space 2} 1.232845{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4} .3025645{col 67}{space 3} 5.135228
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.494569{col 26}{space 2} 1.207724{col 37}{space 1}   -1.24{col 46}{space 3}0.216{col 54}{space 4}-3.861665{col 67}{space 3} .8725268
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.5044791{col 26}{space 2} .1787124{col 37}{space 1}   -2.82{col 46}{space 3}0.005{col 54}{space 4}-.8547488{col 67}{space 3}-.1542093
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.7666027{col 26}{space 2} .5505722{col 37}{space 1}   -1.39{col 46}{space 3}0.164{col 54}{space 4}-1.845704{col 67}{space 3}  .312499
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0046863{col 26}{space 2} .0028761{col 37}{space 1}    1.63{col 46}{space 3}0.103{col 54}{space 4}-.0009507{col 67}{space 3} .0103234
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4510729{col 26}{space 2} 1.669559{col 37}{space 1}    0.27{col 46}{space 3}0.787{col 54}{space 4}-2.821202{col 67}{space 3} 3.723348
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.893021{col 26}{space 2} 5.305867{col 54}{space 4} 2.761815{col 67}{space 3} 28.63545
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2a
{txt}
{com}.  
. melogit partyeffect c.goldenshare  ib1.prepost 1.ideol_cum 1.party_control controls_total  c.jipf c.n_obs if gini==1 & ineqred==1, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-70.587109}  
Iteration 1:{space 2}Log likelihood = {res:-67.787831}  
Iteration 2:{space 2}Log likelihood = {res:-67.766123}  
Iteration 3:{space 2}Log likelihood = {res:-67.766115}  
Iteration 4:{space 2}Log likelihood = {res:-67.766115}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-65.372959}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-65.372959}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-64.321529}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-64.207918}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-64.205069}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-64.205062}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-64.205062}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       159
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        20

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       8.0
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    17.44
{txt}Log pseudolikelihood = {res}-64.205062{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0148
{txt}{ralign 78:(Std. err. adjusted for {res:20} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-7.130527{col 26}{space 2} 4.814399{col 37}{space 1}   -1.48{col 46}{space 3}0.139{col 54}{space 4}-16.56658{col 67}{space 3} 2.305522
{txt}{space 12} {c |}
{space 5}prepost {c |}
{space 3}post-tax  {c |}{col 14}{res}{space 2} .7427952{col 26}{space 2} 1.277548{col 37}{space 1}    0.58{col 46}{space 3}0.561{col 54}{space 4}-1.761153{col 67}{space 3} 3.246743
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2}  2.19776{col 26}{space 2} 1.733072{col 37}{space 1}    1.27{col 46}{space 3}0.205{col 54}{space 4}-1.198999{col 67}{space 3}  5.59452
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-3.192206{col 26}{space 2} 2.225372{col 37}{space 1}   -1.43{col 46}{space 3}0.151{col 54}{space 4}-7.553856{col 67}{space 3} 1.169443
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.4857255{col 26}{space 2} .2710786{col 37}{space 1}   -1.79{col 46}{space 3}0.073{col 54}{space 4} -1.01703{col 67}{space 3} .0455789
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-1.132275{col 26}{space 2} 1.079721{col 37}{space 1}   -1.05{col 46}{space 3}0.294{col 54}{space 4}-3.248489{col 67}{space 3} .9839398
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0096428{col 26}{space 2} .0055768{col 37}{space 1}    1.73{col 46}{space 3}0.084{col 54}{space 4}-.0012875{col 67}{space 3} .0205732
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.857912{col 26}{space 2} 2.173552{col 37}{space 1}    0.85{col 46}{space 3}0.393{col 54}{space 4}-2.402172{col 67}{space 3} 6.117995
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 2.575012{col 26}{space 2} 2.580742{col 54}{space 4} .3611467{col 67}{space 3} 18.36009
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3a 
{txt}
{com}. 
.  cd  "$RESULTSDIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a m3a m2a using Table_SC12_preposttax_control.rtf, label replace  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1" "M2" "M3") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff."  ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
>  1.prepost "Post-tax" controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *toprest  *ideol_cum *party_control *controls_total *prepost)
{res}{txt}(output written to {browse  `"Table_SC12_preposttax_control.rtf"'})

{com}. cd  "$DATADIR"  
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}. 
. *Table S-C13: Explaining partisan effects on inequality: Separate models for measures before/after taxes and transfers
. 
. *pre-tax only
. 
. melogit partyeffect c.goldenshare ib0.party_control i.ineq_meas i.prepost  ib0.ideol_cum  controls_total  c.jipf c.n_obs if prepost==2, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-46.985198}  
Iteration 1:{space 2}Log likelihood = {res:-38.682379}  
Iteration 2:{space 2}Log likelihood = {res: -37.88961}  
Iteration 3:{space 2}Log likelihood = {res: -37.85588}  
Iteration 4:{space 2}Log likelihood = {res:-37.855742}  
Iteration 5:{space 2}Log likelihood = {res:-37.855742}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-39.638534}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-39.638534}  (not concave)
Iteration 1:{space 2}Log pseudolikelihood = {res:-38.502174}  (not concave)
Iteration 2:{space 2}Log pseudolikelihood = {res:-38.030111}  (not concave)
Iteration 3:{space 2}Log pseudolikelihood = {res:-37.931774}  (not concave)
Iteration 4:{space 2}Log pseudolikelihood = {res:-37.891633}  (not concave)
Iteration 5:{space 2}Log pseudolikelihood = {res:-37.875441}  (not concave)
Iteration 6:{space 2}Log pseudolikelihood = {res:-37.862394}  (not concave)
Iteration 7:{space 2}Log pseudolikelihood = {res:-37.861088}  (not concave)
Iteration 8:{space 2}Log pseudolikelihood = {res:-37.860566}  (not concave)
Iteration 9:{space 2}Log pseudolikelihood = {res:-37.860357}  (not concave)
Iteration 10:{space 1}Log pseudolikelihood = {res:-37.860315}  (not concave)
Iteration 11:{space 1}Log pseudolikelihood = {res:-37.860299}  (not concave)
Iteration 12:{space 1}Log pseudolikelihood = {res:-37.860292}  (not concave)
Iteration 13:{space 1}Log pseudolikelihood = {res:-37.860291}  (not concave)
Iteration 14:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 15:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 16:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 17:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 18:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 19:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 20:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 21:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 22:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 23:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 24:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 25:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 26:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 27:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 28:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 29:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 30:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 31:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 32:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 33:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 34:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 35:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 36:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 37:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 38:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 39:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 40:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 41:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 42:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 43:{space 1}Log pseudolikelihood = {res: -37.86029}  
Iteration 44:{space 1}Log pseudolikelihood = {res:-37.860289}  (not concave)
Iteration 45:{space 1}Log pseudolikelihood = {res:-37.860288}  (not concave)
Iteration 46:{space 1}Log pseudolikelihood = {res:-37.860287}  
Iteration 47:{space 1}Log pseudolikelihood = {res:-37.860251}  (not concave)
Iteration 48:{space 1}Log pseudolikelihood = {res: -37.86025}  
Iteration 49:{space 1}Log pseudolikelihood = {res:-37.859974}  
Iteration 50:{space 1}Log pseudolikelihood = {res:-37.859714}  (not concave)
Iteration 51:{space 1}Log pseudolikelihood = {res:-37.859663}  
Iteration 52:{space 1}Log pseudolikelihood = {res:-37.855742}  (not concave)
Iteration 53:{space 1}Log pseudolikelihood = {res:-37.855742}  (backed up)
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       118
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        13

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        33

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}   153.41
{txt}Log pseudolikelihood = {res}-37.855742{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:13} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-30.51088{col 26}{space 2} 7.854612{col 37}{space 1}   -3.88{col 46}{space 3}0.000{col 54}{space 4}-45.90563{col 67}{space 3}-15.11612
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-11.19163{col 26}{space 2}  2.53337{col 37}{space 1}   -4.42{col 46}{space 3}0.000{col 54}{space 4}-16.15694{col 67}{space 3}-6.226316
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .3058563{col 26}{space 2} .4974901{col 37}{space 1}    0.61{col 46}{space 3}0.539{col 54}{space 4}-.6692063{col 67}{space 3} 1.280919
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 9.341667{col 26}{space 2} 2.438101{col 37}{space 1}    3.83{col 46}{space 3}0.000{col 54}{space 4} 4.563077{col 67}{space 3} 14.12026
{txt}controls_t~l {c |}{col 14}{res}{space 2}-1.142195{col 26}{space 2} .4827446{col 37}{space 1}   -2.37{col 46}{space 3}0.018{col 54}{space 4}-2.088357{col 67}{space 3}-.1960331
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-4.972588{col 26}{space 2} 1.100498{col 37}{space 1}   -4.52{col 46}{space 3}0.000{col 54}{space 4}-7.129524{col 67}{space 3}-2.815652
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0305336{col 26}{space 2} .0067388{col 37}{space 1}    4.53{col 46}{space 3}0.000{col 54}{space 4} .0173258{col 67}{space 3} .0437413
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 10.82124{col 26}{space 2} 3.115997{col 37}{space 1}    3.47{col 46}{space 3}0.001{col 54}{space 4} 4.713996{col 67}{space 3} 16.92848
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 2.97e-33{col 26}{space 2} 1.75e-32{col 54}{space 4} 2.91e-38{col 67}{space 3} 3.03e-28
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1a
{txt}
{com}.  
. melogit partyeffect c.goldenshare ib0.party_control i.ineq_meas i.prepost  ib0.ideol_cum  controls_total  c.jipf c.n_obs if prepost==1, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-116.29325}  
Iteration 1:{space 2}Log likelihood = {res:-115.40818}  
Iteration 2:{space 2}Log likelihood = {res:-115.40249}  
Iteration 3:{space 2}Log likelihood = {res:-115.40249}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-103.16715}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-103.16715}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-97.943299}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-96.581437}  
Iteration 3:{space 2}Log pseudolikelihood = {res:  -96.3561}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-96.349513}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-96.349483}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-96.349474}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       232
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        30

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.7
{col 63}{txt}max{col 67}={res}{col 69}        27

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    16.75
{txt}Log pseudolikelihood = {res}-96.349474{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0328
{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}  -2.5243{col 26}{space 2} 3.741922{col 37}{space 1}   -0.67{col 46}{space 3}0.500{col 54}{space 4}-9.858333{col 67}{space 3} 4.809734
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.450446{col 26}{space 2} 1.256106{col 37}{space 1}   -1.15{col 46}{space 3}0.248{col 54}{space 4}-3.912369{col 67}{space 3} 1.011478
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2}  .778994{col 26}{space 2} .7715763{col 37}{space 1}    1.01{col 46}{space 3}0.313{col 54}{space 4}-.7332679{col 67}{space 3} 2.291256
{txt}{space 6}Share  {c |}{col 14}{res}{space 2}  2.90684{col 26}{space 2} 1.061061{col 37}{space 1}    2.74{col 46}{space 3}0.006{col 54}{space 4} .8271978{col 67}{space 3} 4.986482
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.248383{col 26}{space 2} 1.147394{col 37}{space 1}    1.96{col 46}{space 3}0.050{col 54}{space 4}-.0004687{col 67}{space 3} 4.497235
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.4024235{col 26}{space 2} .1575053{col 37}{space 1}   -2.55{col 46}{space 3}0.011{col 54}{space 4}-.7111281{col 67}{space 3}-.0937188
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.2902815{col 26}{space 2} .6000231{col 37}{space 1}   -0.48{col 46}{space 3}0.629{col 54}{space 4}-1.466305{col 67}{space 3} .8857421
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0022952{col 26}{space 2} .0028027{col 37}{space 1}    0.82{col 46}{space 3}0.413{col 54}{space 4}-.0031979{col 67}{space 3} .0077883
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1608258{col 26}{space 2} 1.843552{col 37}{space 1}   -0.09{col 46}{space 3}0.930{col 54}{space 4}-3.774121{col 67}{space 3} 3.452469
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.087826{col 26}{space 2} 5.009877{col 54}{space 4} 2.401974{col 67}{space 3} 27.23298
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1b 
{txt}
{com}. 
. melogit partyeffect c.goldenshare 1.toprest  ib0.ideol_cum ib0.party_control controls_total c.jipf c.n_obs if prepost==2, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-46.985198}  
Iteration 1:{space 2}Log likelihood = {res:-38.682379}  
Iteration 2:{space 2}Log likelihood = {res: -37.88961}  
Iteration 3:{space 2}Log likelihood = {res: -37.85588}  
Iteration 4:{space 2}Log likelihood = {res:-37.855742}  
Iteration 5:{space 2}Log likelihood = {res:-37.855742}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-39.638534}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-39.638534}  (not concave)
Iteration 1:{space 2}Log pseudolikelihood = {res:-38.502174}  (not concave)
Iteration 2:{space 2}Log pseudolikelihood = {res:-38.030111}  (not concave)
Iteration 3:{space 2}Log pseudolikelihood = {res:-37.931774}  (not concave)
Iteration 4:{space 2}Log pseudolikelihood = {res:-37.891633}  (not concave)
Iteration 5:{space 2}Log pseudolikelihood = {res:-37.875441}  (not concave)
Iteration 6:{space 2}Log pseudolikelihood = {res:-37.862394}  (not concave)
Iteration 7:{space 2}Log pseudolikelihood = {res:-37.861088}  (not concave)
Iteration 8:{space 2}Log pseudolikelihood = {res:-37.860566}  (not concave)
Iteration 9:{space 2}Log pseudolikelihood = {res:-37.860357}  (not concave)
Iteration 10:{space 1}Log pseudolikelihood = {res:-37.860315}  (not concave)
Iteration 11:{space 1}Log pseudolikelihood = {res:-37.860299}  (not concave)
Iteration 12:{space 1}Log pseudolikelihood = {res:-37.860292}  (not concave)
Iteration 13:{space 1}Log pseudolikelihood = {res:-37.860291}  (not concave)
Iteration 14:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 15:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 16:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 17:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 18:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 19:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 20:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 21:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 22:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 23:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 24:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 25:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 26:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 27:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 28:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 29:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 30:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 31:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 32:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 33:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 34:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 35:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 36:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 37:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 38:{space 1}Log pseudolikelihood = {res: -37.86029}  (backed up)
Iteration 39:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 40:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 41:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 42:{space 1}Log pseudolikelihood = {res: -37.86029}  (not concave)
Iteration 43:{space 1}Log pseudolikelihood = {res:-37.860289}  
Iteration 44:{space 1}Log pseudolikelihood = {res:-37.860272}  (backed up)
Iteration 45:{space 1}Log pseudolikelihood = {res:-37.860236}  (not concave)
Iteration 46:{space 1}Log pseudolikelihood = {res:-37.860235}  (not concave)
Iteration 47:{space 1}Log pseudolikelihood = {res:-37.860233}  
Iteration 48:{space 1}Log pseudolikelihood = {res:-37.859958}  (not concave)
Iteration 49:{space 1}Log pseudolikelihood = {res:-37.859956}  
Iteration 50:{space 1}Log pseudolikelihood = {res:-37.859891}  (backed up)
Iteration 51:{space 1}Log pseudolikelihood = {res:-37.859763}  (backed up)
Iteration 52:{space 1}Log pseudolikelihood = {res:-37.858011}  (not concave)
Iteration 53:{space 1}Log pseudolikelihood = {res:-37.857924}  
Iteration 54:{space 1}Log pseudolikelihood = {res:-37.855742}  (not concave)
Iteration 55:{space 1}Log pseudolikelihood = {res:-37.855742}  (backed up)
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       118
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        13

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        33

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}   153.32
{txt}Log pseudolikelihood = {res}-37.855742{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:13} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-30.51258{col 26}{space 2} 7.855205{col 37}{space 1}   -3.88{col 46}{space 3}0.000{col 54}{space 4}-45.90849{col 67}{space 3}-15.11666
{txt}{space 3}1.toprest {c |}{col 14}{res}{space 2} .3058516{col 26}{space 2} .4974893{col 37}{space 1}    0.61{col 46}{space 3}0.539{col 54}{space 4}-.6692094{col 67}{space 3} 1.280913
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 9.342115{col 26}{space 2} 2.438313{col 37}{space 1}    3.83{col 46}{space 3}0.000{col 54}{space 4}  4.56311{col 67}{space 3} 14.12112
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-11.19224{col 26}{space 2} 2.533562{col 37}{space 1}   -4.42{col 46}{space 3}0.000{col 54}{space 4}-16.15793{col 67}{space 3} -6.22655
{txt}controls_t~l {c |}{col 14}{res}{space 2}-1.142223{col 26}{space 2} .4827829{col 37}{space 1}   -2.37{col 46}{space 3}0.018{col 54}{space 4}-2.088461{col 67}{space 3}-.1959863
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-4.972934{col 26}{space 2} 1.100625{col 37}{space 1}   -4.52{col 46}{space 3}0.000{col 54}{space 4}-7.130119{col 67}{space 3}-2.815749
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0305351{col 26}{space 2} .0067394{col 37}{space 1}    4.53{col 46}{space 3}0.000{col 54}{space 4} .0173262{col 67}{space 3}  .043744
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 10.82176{col 26}{space 2} 3.116377{col 37}{space 1}    3.47{col 46}{space 3}0.001{col 54}{space 4} 4.713776{col 67}{space 3} 16.92975
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 2.57e-34{col 26}{space 2} 9.51e-34{col 54}{space 4} 1.80e-37{col 67}{space 3} 3.66e-31
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2a
{txt}
{com}.  
. *post-tax only
.  
. melogit partyeffect c.goldenshare 1.toprest  ib0.ideol_cum ib0.party_control controls_total c.jipf c.n_obs if prepost==1, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res: -116.3577}  
Iteration 1:{space 2}Log likelihood = {res:-115.33468}  
Iteration 2:{space 2}Log likelihood = {res:-115.33015}  
Iteration 3:{space 2}Log likelihood = {res:-115.33015}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-97.959877}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-97.959877}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-89.474109}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-86.504276}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-85.351086}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-85.193342}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-85.202955}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-85.206094}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-85.206944}  
Iteration 8:{space 2}Log pseudolikelihood = {res:-85.207263}  
Iteration 9:{space 2}Log pseudolikelihood = {res:-85.207393}  
Iteration 10:{space 1}Log pseudolikelihood = {res:-85.207449}  
Iteration 11:{space 1}Log pseudolikelihood = {res:-85.207473}  
Iteration 12:{space 1}Log pseudolikelihood = {res:-85.207484}  
Iteration 13:{space 1}Log pseudolikelihood = {res:-85.207488}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       232
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        30

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.7
{col 63}{txt}max{col 67}={res}{col 69}        27

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    11.75
{txt}Log pseudolikelihood = {res}-85.207488{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.1092
{txt}{ralign 78:(Std. err. adjusted for {res:30} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-3.563228{col 26}{space 2} 4.794976{col 37}{space 1}   -0.74{col 46}{space 3}0.457{col 54}{space 4}-12.96121{col 67}{space 3} 5.834752
{txt}{space 3}1.toprest {c |}{col 14}{res}{space 2} 4.442171{col 26}{space 2} 1.591623{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} 1.322648{col 67}{space 3} 7.561694
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.394688{col 26}{space 2} 1.658279{col 37}{space 1}    2.05{col 46}{space 3}0.041{col 54}{space 4} .1445215{col 67}{space 3} 6.644855
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.465916{col 26}{space 2} 1.624731{col 37}{space 1}   -1.52{col 46}{space 3}0.129{col 54}{space 4} -5.65033{col 67}{space 3}  .718499
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.5634073{col 26}{space 2} .2267615{col 37}{space 1}   -2.48{col 46}{space 3}0.013{col 54}{space 4}-1.007852{col 67}{space 3}-.1189629
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} -.226966{col 26}{space 2} .6771456{col 37}{space 1}   -0.34{col 46}{space 3}0.737{col 54}{space 4}-1.554147{col 67}{space 3} 1.100215
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0044479{col 26}{space 2} .0038345{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4}-.0030675{col 67}{space 3} .0119633
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.430238{col 26}{space 2} 2.121935{col 37}{space 1}   -0.67{col 46}{space 3}0.500{col 54}{space 4}-5.589154{col 67}{space 3} 2.728678
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 16.72499{col 26}{space 2} 12.64231{col 54}{space 4}  3.80144{col 67}{space 3} 73.58402
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2b 
{txt}
{com}.  
. 
.  cd  "$RESULTSDIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a m1b m2a m2b using Table_SC13_preposttax_separate.rtf, label replace  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1 (pre)" "M1 (post)" "M2 (pre)" "M2 (post)") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff."  ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
>  1.prepost "Post-tax" controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *toprest  *ideol_cum *party_control *controls_total)
{res}{txt}(output written to {browse  `"Table_SC13_preposttax_separate.rtf"'})

{com}. cd  "$DATADIR"   
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.  
. * Table S-C14: Explaining partisan effects on inequality: Top-income recoded (Top-1-Top10% Income Shares and Ratios vs. Rest) 
. melogit partyeffect c.goldenshare ib0.party_control 1.toptop  ib0.ideol_cum  controls_total  c.jipf c.n_obs, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res: -210.6423}  
Iteration 1:{space 2}Log likelihood = {res:-210.06533}  
Iteration 2:{space 2}Log likelihood = {res:-210.06463}  
Iteration 3:{space 2}Log likelihood = {res:-210.06463}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-174.11114}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-174.11114}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-165.36992}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-161.95983}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-161.59204}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-161.58756}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-161.58751}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-161.58751}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    20.88
{txt}Log pseudolikelihood = {res}-161.58751{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0039
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2} -3.08545{col 26}{space 2} 2.822823{col 37}{space 1}   -1.09{col 46}{space 3}0.274{col 54}{space 4}-8.618082{col 67}{space 3} 2.447182
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.137211{col 26}{space 2} 1.024489{col 37}{space 1}   -2.09{col 46}{space 3}0.037{col 54}{space 4}-4.145171{col 67}{space 3}-.1292501
{txt}{space 4}1.toptop {c |}{col 14}{res}{space 2} 2.047084{col 26}{space 2} .9779189{col 37}{space 1}    2.09{col 46}{space 3}0.036{col 54}{space 4} .1303981{col 67}{space 3}  3.96377
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.986255{col 26}{space 2}  1.01585{col 37}{space 1}    2.94{col 46}{space 3}0.003{col 54}{space 4} .9952257{col 67}{space 3} 4.977285
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3268657{col 26}{space 2} .1596915{col 37}{space 1}   -2.05{col 46}{space 3}0.041{col 54}{space 4}-.6398552{col 67}{space 3}-.0138761
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.5232283{col 26}{space 2} .3336989{col 37}{space 1}   -1.57{col 46}{space 3}0.117{col 54}{space 4}-1.177266{col 67}{space 3} .1308096
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0045072{col 26}{space 2}  .002452{col 37}{space 1}    1.84{col 46}{space 3}0.066{col 54}{space 4}-.0002986{col 67}{space 3}  .009313
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1489387{col 26}{space 2} 1.406188{col 37}{space 1}   -0.11{col 46}{space 3}0.916{col 54}{space 4}-2.905016{col 67}{space 3} 2.607138
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 7.394787{col 26}{space 2} 3.969466{col 54}{space 4} 2.582302{col 67}{space 3} 21.17602
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1a
{txt}
{com}.  
. melogit partyeffect c.goldenshare ib0.party_control 1.toptop  ib0.ideol_cum  controls_total  c.jipf c.n_obs if ineq_meas>1, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-103.58434}  
Iteration 1:{space 2}Log likelihood = {res:-102.31183}  
Iteration 2:{space 2}Log likelihood = {res:-102.29843}  
Iteration 3:{space 2}Log likelihood = {res:-102.29842}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-86.561531}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-86.561531}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -80.73958}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-78.981841}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-78.429622}  
Iteration 4:{space 2}Log pseudolikelihood = {res:  -78.3808}  
Iteration 5:{space 2}Log pseudolikelihood = {res: -78.37987}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-78.379947}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-78.379962}  
Iteration 8:{space 2}Log pseudolikelihood = {res:-78.379966}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       205
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        25

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       8.2
{col 63}{txt}max{col 67}={res}{col 69}        27

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}7{txt}){col 67}={res}{col 70}    11.95
{txt}Log pseudolikelihood = {res}-78.379966{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.1021
{txt}{ralign 78:(Std. err. adjusted for {res:25} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-1.249514{col 26}{space 2} 3.912881{col 37}{space 1}   -0.32{col 46}{space 3}0.749{col 54}{space 4}-8.918619{col 67}{space 3} 6.419591
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.289935{col 26}{space 2} 1.528161{col 37}{space 1}   -1.50{col 46}{space 3}0.134{col 54}{space 4}-5.285076{col 67}{space 3} .7052058
{txt}{space 4}1.toptop {c |}{col 14}{res}{space 2} 3.817372{col 26}{space 2}  2.09015{col 37}{space 1}    1.83{col 46}{space 3}0.068{col 54}{space 4}-.2792456{col 67}{space 3}  7.91399
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.385919{col 26}{space 2} 1.130582{col 37}{space 1}    2.99{col 46}{space 3}0.003{col 54}{space 4} 1.170018{col 67}{space 3}  5.60182
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3420389{col 26}{space 2} .2331414{col 37}{space 1}   -1.47{col 46}{space 3}0.142{col 54}{space 4}-.7989875{col 67}{space 3} .1149098
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.2999797{col 26}{space 2} .3567893{col 37}{space 1}   -0.84{col 46}{space 3}0.400{col 54}{space 4} -.999274{col 67}{space 3} .3993145
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0039081{col 26}{space 2} .0029742{col 37}{space 1}    1.31{col 46}{space 3}0.189{col 54}{space 4}-.0019213{col 67}{space 3} .0097375
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.926173{col 26}{space 2} 2.092278{col 37}{space 1}   -1.40{col 46}{space 3}0.162{col 54}{space 4}-7.026962{col 67}{space 3} 1.174616
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2}  8.82554{col 26}{space 2} 7.038903{col 54}{space 4} 1.848657{col 67}{space 3} 42.13336
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2a
{txt}
{com}.  
. 
.  cd  "$RESULTSDIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a  m2a using Table_SC14_toptop.rtf, label replace  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1" "M2" "M3") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff."  ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
> controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest  *ideol_cum *party_control *controls_total)
{res}{txt}(output written to {browse  `"Table_SC14_toptop.rtf"'})

{com}. cd  "$DATADIR"  
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.  
.  
. *Table S-C15: Explaining partisan effects on inequality: Multi-level meta regression based on available standardized coefficients 
. meta meregress eff_std c.goldenshare ib0.party_control i.ineq_meas   ib0.ideol_cum  controls_total  c.jipf c.n_obs ,  ||paper_id:, essevariable(se_std) 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log restricted-likelihood = {res: 125.33095}  
Iteration 1:{space 2}Log restricted-likelihood = {res: 132.94441}  
Iteration 2:{space 2}Log restricted-likelihood = {res: 132.97514}  
Iteration 3:{space 2}Log restricted-likelihood = {res: 132.97516}  
Iteration 4:{space 2}Log restricted-likelihood = {res: 132.97516}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Multilevel REML meta-regression{col 54}Number of obs{col 70} = {res}   174
{txt}Group variable: {res}paper_id{col 54}{txt}Number of groups{col 70} = {res}    20
{txt}{col 54}Obs per group:
{col 67}min = {res}     1
{txt}{col 67}avg = {res}   8.7
{txt}{col 67}max = {res}    37
{col 54}{txt}Wald chi2({res}8{txt}){col 70} = {res} 21.29
{txt}Log restricted-likelihood = {res} 132.97516{col 54}{txt}Prob > chi2{col 70} = {res}0.0064

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     eff_std{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-.0011558{col 26}{space 2} .0242319{col 37}{space 1}   -0.05{col 46}{space 3}0.962{col 54}{space 4}-.0486494{col 67}{space 3} .0463378
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.0476495{col 26}{space 2} .0534793{col 37}{space 1}   -0.89{col 46}{space 3}0.373{col 54}{space 4}-.1524671{col 67}{space 3}  .057168
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .0714848{col 26}{space 2} .0191008{col 37}{space 1}    3.74{col 46}{space 3}0.000{col 54}{space 4}  .034048{col 67}{space 3} .1089217
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} .0814873{col 26}{space 2} .0201244{col 37}{space 1}    4.05{col 46}{space 3}0.000{col 54}{space 4} .0420442{col 67}{space 3} .1209304
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} .1294743{col 26}{space 2} .0608392{col 37}{space 1}    2.13{col 46}{space 3}0.033{col 54}{space 4} .0102317{col 67}{space 3}  .248717
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.0000841{col 26}{space 2} .0002032{col 37}{space 1}   -0.41{col 46}{space 3}0.679{col 54}{space 4}-.0004824{col 67}{space 3} .0003141
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} -.005333{col 26}{space 2} .0182524{col 37}{space 1}   -0.29{col 46}{space 3}0.770{col 54}{space 4} -.041107{col 67}{space 3}  .030441
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2}-2.31e-06{col 26}{space 2} .0000109{col 37}{space 1}   -0.21{col 46}{space 3}0.832{col 54}{space 4}-.0000236{col 67}{space 3}  .000019
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -.022859{col 26}{space 2} .0572426{col 37}{space 1}   -0.40{col 46}{space 3}0.690{col 54}{space 4}-.1350523{col 67}{space 3} .0893344
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}Test of homogeneity: Q_M = chi2({res}165{txt}) = {res}694.00{txt}{col 60}Prob > Q_M = {res}0.0000

{txt}{hline 29}{c TT}{hline 11}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate
{hline 29}{c +}{hline 11}
{res}paper_id{txt}: Identity{col 30}{c |}
{space 19}sd(_cons) {c |}{res}{col 33} .1119691
{txt}{hline 29}{c BT}{hline 11}

{com}. est store m1a
{txt}
{com}. 
. meta meregress eff_std c.goldenshare toprest  ib0.ideol_cum ib0.party_control controls_total c.jipf c.n_obs   ,  ||paper_id:, essevariable(se_std) 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log restricted-likelihood = {res: 128.02697}  
Iteration 1:{space 2}Log restricted-likelihood = {res: 134.88404}  
Iteration 2:{space 2}Log restricted-likelihood = {res:  134.9635}  
Iteration 3:{space 2}Log restricted-likelihood = {res: 134.96396}  
Iteration 4:{space 2}Log restricted-likelihood = {res: 134.96396}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Multilevel REML meta-regression{col 54}Number of obs{col 70} = {res}   174
{txt}Group variable: {res}paper_id{col 54}{txt}Number of groups{col 70} = {res}    20
{txt}{col 54}Obs per group:
{col 67}min = {res}     1
{txt}{col 67}avg = {res}   8.7
{txt}{col 67}max = {res}    37
{col 54}{txt}Wald chi2({res}7{txt}){col 70} = {res} 22.11
{txt}Log restricted-likelihood = {res} 134.96396{col 54}{txt}Prob > chi2{col 70} = {res}0.0024

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     eff_std{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2} .0024225{col 26}{space 2} .0241303{col 37}{space 1}    0.10{col 46}{space 3}0.920{col 54}{space 4} -.044872{col 67}{space 3} .0497171
{txt}{space 5}toprest {c |}{col 14}{res}{space 2}  .026103{col 26}{space 2} .0065711{col 37}{space 1}    3.97{col 46}{space 3}0.000{col 54}{space 4} .0132239{col 67}{space 3} .0389821
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} .1075816{col 26}{space 2} .0490644{col 37}{space 1}    2.19{col 46}{space 3}0.028{col 54}{space 4} .0114171{col 67}{space 3}  .203746
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.0570409{col 26}{space 2} .0449291{col 37}{space 1}   -1.27{col 46}{space 3}0.204{col 54}{space 4}-.1451002{col 67}{space 3} .0310185
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.0000893{col 26}{space 2} .0002032{col 37}{space 1}   -0.44{col 46}{space 3}0.660{col 54}{space 4}-.0004876{col 67}{space 3}  .000309
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} -.003725{col 26}{space 2}  .014662{col 37}{space 1}   -0.25{col 46}{space 3}0.799{col 54}{space 4} -.032462{col 67}{space 3}  .025012
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2}-4.24e-06{col 26}{space 2} .0000108{col 37}{space 1}   -0.39{col 46}{space 3}0.695{col 54}{space 4}-.0000254{col 67}{space 3} .0000169
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0199481{col 26}{space 2} .0449909{col 37}{space 1}    0.44{col 46}{space 3}0.657{col 54}{space 4}-.0682324{col 67}{space 3} .1081287
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}Test of homogeneity: Q_M = chi2({res}166{txt}) = {res}747.82{txt}{col 60}Prob > Q_M = {res}0.0000

{txt}{hline 29}{c TT}{hline 11}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate
{hline 29}{c +}{hline 11}
{res}paper_id{txt}: Identity{col 30}{c |}
{space 19}sd(_cons) {c |}{res}{col 33} .0898841
{txt}{hline 29}{c BT}{hline 11}

{com}. est store m2a
{txt}
{com}. 
. meta meregress eff_std c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total c.jipf c.n_obs,  ||paper_id:, essevariable(se_std) 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log restricted-likelihood = {res: 43.736746}  
Iteration 1:{space 2}Log restricted-likelihood = {res: 43.770786}  
Iteration 2:{space 2}Log restricted-likelihood = {res: 43.770876}  
Iteration 3:{space 2}Log restricted-likelihood = {res: 43.770876}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Multilevel REML meta-regression{col 54}Number of obs{col 70} = {res}    48
{txt}Group variable: {res}paper_id{col 54}{txt}Number of groups{col 70} = {res}     8
{txt}{col 54}Obs per group:
{col 67}min = {res}     1
{txt}{col 67}avg = {res}   6.0
{txt}{col 67}max = {res}    13
{col 54}{txt}Wald chi2({res}8{txt}){col 70} = {res} 11.54
{txt}Log restricted-likelihood = {res} 43.770876{col 54}{txt}Prob > chi2{col 70} = {res}0.1729

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     eff_std{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2} 1.949637{col 26}{space 2} 3.115481{col 37}{space 1}    0.63{col 46}{space 3}0.531{col 54}{space 4}-4.156593{col 67}{space 3} 8.055866
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2}-.0422047{col 26}{space 2} .0181692{col 37}{space 1}   -2.32{col 46}{space 3}0.020{col 54}{space 4}-.0778157{col 67}{space 3}-.0065936
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} -.005383{col 26}{space 2} .1000243{col 37}{space 1}   -0.05{col 46}{space 3}0.957{col 54}{space 4}-.2014271{col 67}{space 3}  .190661
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} .1524948{col 26}{space 2} .7003843{col 37}{space 1}    0.22{col 46}{space 3}0.828{col 54}{space 4}-1.220233{col 67}{space 3} 1.525223
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.1970214{col 26}{space 2} 1.065743{col 37}{space 1}   -0.18{col 46}{space 3}0.853{col 54}{space 4} -2.28584{col 67}{space 3} 1.891797
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.0001191{col 26}{space 2} .0144138{col 37}{space 1}   -0.01{col 46}{space 3}0.993{col 54}{space 4}-.0283696{col 67}{space 3} .0281314
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}  .380529{col 26}{space 2} .4567256{col 37}{space 1}    0.83{col 46}{space 3}0.405{col 54}{space 4}-.5146367{col 67}{space 3} 1.275695
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2}  .000156{col 26}{space 2}  .002674{col 37}{space 1}    0.06{col 46}{space 3}0.953{col 54}{space 4} -.005085{col 67}{space 3} .0053969
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.8549879{col 26}{space 2} 1.351535{col 37}{space 1}   -0.63{col 46}{space 3}0.527{col 54}{space 4}-3.503948{col 67}{space 3} 1.793972
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}Test of homogeneity: Q_M = chi2({res}39{txt}) = {res}92.94{txt}{col 60}Prob > Q_M = {res}0.0000

{txt}{hline 29}{c TT}{hline 11}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate
{hline 29}{c +}{hline 11}
{res}paper_id{txt}: Identity{col 30}{c |}
{space 19}sd(_cons) {c |}{res}{col 33} .7680909
{txt}{hline 29}{c BT}{hline 11}

{com}. est store m3a 
{txt}
{com}. 
. cd  "$RESULTSDIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a  m3a  m2a using Table_SC15_ML_meta_analysis.rtf, label replace  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1" "M2" "M3") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff." ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
>  controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest  *ideol_cum *party_control *controls_total)
{res}{txt}(output written to {browse  `"Table_SC15_ML_meta_analysis.rtf"'})

{com}. cd  "$DATADIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.  
. *Table S-C16: Explaining partisan effects on inequality: Controlling for models with/without country-fixed effects 
. 
. 
. melogit partyeffect c.goldenshare ib0.party_control i.ineq_meas  ib0.ideol_cum  controls_total 1.country_FE c.jipf c.n_obs , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-213.03036}  
Iteration 1:{space 2}Log likelihood = {res: -212.3451}  
Iteration 2:{space 2}Log likelihood = {res:-212.34416}  
Iteration 3:{space 2}Log likelihood = {res:-212.34416}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-177.84835}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-177.84835}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-169.62593}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-167.59325}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-167.26611}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-167.25896}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-167.25931}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-167.25933}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-167.25934}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}    27.46
{txt}Log pseudolikelihood = {res}-167.25934{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0012
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2} -3.42407{col 26}{space 2} 3.067707{col 37}{space 1}   -1.12{col 46}{space 3}0.264{col 54}{space 4}-9.436665{col 67}{space 3} 2.588525
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.894651{col 26}{space 2} 1.022167{col 37}{space 1}   -1.85{col 46}{space 3}0.064{col 54}{space 4}-3.898062{col 67}{space 3}  .108759
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .4884288{col 26}{space 2} .3490795{col 37}{space 1}    1.40{col 46}{space 3}0.162{col 54}{space 4}-.1957544{col 67}{space 3} 1.172612
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.362095{col 26}{space 2} .9281335{col 37}{space 1}    2.54{col 46}{space 3}0.011{col 54}{space 4} .5429865{col 67}{space 3} 4.181203
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.139244{col 26}{space 2} 1.038051{col 37}{space 1}    3.02{col 46}{space 3}0.002{col 54}{space 4} 1.104701{col 67}{space 3} 5.173787
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2712282{col 26}{space 2} .1515529{col 37}{space 1}   -1.79{col 46}{space 3}0.074{col 54}{space 4}-.5682664{col 67}{space 3}   .02581
{txt}{space 12} {c |}
{space 2}country_FE {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.2840997{col 26}{space 2} .6393833{col 37}{space 1}   -0.44{col 46}{space 3}0.657{col 54}{space 4}-1.537268{col 67}{space 3} .9690685
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6474392{col 26}{space 2} .3469338{col 37}{space 1}   -1.87{col 46}{space 3}0.062{col 54}{space 4}-1.327417{col 67}{space 3} .0325387
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2}  .004184{col 26}{space 2} .0024488{col 37}{space 1}    1.71{col 46}{space 3}0.088{col 54}{space 4}-.0006155{col 67}{space 3} .0089836
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0223969{col 26}{space 2}   1.3846{col 37}{space 1}    0.02{col 46}{space 3}0.987{col 54}{space 4}-2.691369{col 67}{space 3} 2.736163
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 7.297734{col 26}{space 2} 3.710513{col 54}{space 4} 2.693995{col 67}{space 3} 19.76875
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1a
{txt}
{com}. 
. melogit partyeffect c.goldenshare toprest  ib0.ideol_cum ib0.party_control controls_total 1.country_FE c.jipf c.n_obs , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res: -208.4709}  
Iteration 1:{space 2}Log likelihood = {res:-207.80469}  
Iteration 2:{space 2}Log likelihood = {res:-207.80375}  
Iteration 3:{space 2}Log likelihood = {res:-207.80375}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-173.25462}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-173.25462}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-163.89711}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-160.83632}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-160.03628}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-159.96234}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-159.96339}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-159.96345}  
Iteration 7:{space 2}Log pseudolikelihood = {res:-159.96346}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    18.80
{txt}Log pseudolikelihood = {res}-159.96346{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0160
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.290268{col 26}{space 2} 2.905146{col 37}{space 1}   -0.79{col 46}{space 3}0.430{col 54}{space 4}-7.984249{col 67}{space 3} 3.403713
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} 2.367293{col 26}{space 2} 1.103144{col 37}{space 1}    2.15{col 46}{space 3}0.032{col 54}{space 4} .2051699{col 67}{space 3} 4.529415
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.165623{col 26}{space 2} 1.064504{col 37}{space 1}    2.97{col 46}{space 3}0.003{col 54}{space 4} 1.079235{col 67}{space 3} 5.252012
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.858448{col 26}{space 2} 1.099252{col 37}{space 1}   -1.69{col 46}{space 3}0.091{col 54}{space 4}-4.012943{col 67}{space 3} .2960459
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3428501{col 26}{space 2} .1673449{col 37}{space 1}   -2.05{col 46}{space 3}0.040{col 54}{space 4}-.6708401{col 67}{space 3}-.0148602
{txt}{space 12} {c |}
{space 2}country_FE {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-.3587509{col 26}{space 2} .6914562{col 37}{space 1}   -0.52{col 46}{space 3}0.604{col 54}{space 4} -1.71398{col 67}{space 3} .9964783
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6187961{col 26}{space 2} .3475479{col 37}{space 1}   -1.78{col 46}{space 3}0.075{col 54}{space 4}-1.299977{col 67}{space 3} .0623853
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0045602{col 26}{space 2} .0024464{col 37}{space 1}    1.86{col 46}{space 3}0.062{col 54}{space 4}-.0002346{col 67}{space 3}  .009355
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2941586{col 26}{space 2} 1.446635{col 37}{space 1}   -0.20{col 46}{space 3}0.839{col 54}{space 4} -3.12951{col 67}{space 3} 2.541193
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 8.318266{col 26}{space 2} 4.716716{col 54}{space 4} 2.737631{col 67}{space 3} 25.27497
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2a
{txt}
{com}.  
. melogit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total 1.country_FE c.jipf c.n_obs, vce(robust) ||paper_id: 
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-86.410597}  
Iteration 1:{space 2}Log likelihood = {res:-83.968893}  
Iteration 2:{space 2}Log likelihood = {res:-83.960955}  
Iteration 3:{space 2}Log likelihood = {res:-83.960955}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-79.679868}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-79.679868}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-78.581542}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-78.431779}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-78.428007}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-78.427992}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-78.427992}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       188
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.8
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}    47.69
{txt}Log pseudolikelihood = {res}-78.427992{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-4.540133{col 26}{space 2} 3.862526{col 37}{space 1}   -1.18{col 46}{space 3}0.240{col 54}{space 4}-12.11055{col 67}{space 3} 3.030279
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .7375211{col 26}{space 2} 1.231354{col 37}{space 1}    0.60{col 46}{space 3}0.549{col 54}{space 4}-1.675889{col 67}{space 3} 3.150931
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} 1.046954{col 26}{space 2} .7678639{col 37}{space 1}    1.36{col 46}{space 3}0.173{col 54}{space 4}-.4580314{col 67}{space 3}  2.55194
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 1.806308{col 26}{space 2} 1.327369{col 37}{space 1}    1.36{col 46}{space 3}0.174{col 54}{space 4} -.795288{col 67}{space 3} 4.407904
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-2.908383{col 26}{space 2} 1.728815{col 37}{space 1}   -1.68{col 46}{space 3}0.093{col 54}{space 4}-6.296799{col 67}{space 3} .4800326
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3534356{col 26}{space 2}  .216554{col 37}{space 1}   -1.63{col 46}{space 3}0.103{col 54}{space 4}-.7778736{col 67}{space 3} .0710025
{txt}{space 12} {c |}
{space 2}country_FE {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.228543{col 26}{space 2} .7231116{col 37}{space 1}   -1.70{col 46}{space 3}0.089{col 54}{space 4}-2.645816{col 67}{space 3} .1887296
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} -.191943{col 26}{space 2} .8014176{col 37}{space 1}   -0.24{col 46}{space 3}0.811{col 54}{space 4}-1.762693{col 67}{space 3} 1.378807
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0083717{col 26}{space 2} .0041335{col 37}{space 1}    2.03{col 46}{space 3}0.043{col 54}{space 4} .0002701{col 67}{space 3} .0164732
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2721635{col 26}{space 2} 2.016256{col 37}{space 1}    0.13{col 46}{space 3}0.893{col 54}{space 4}-3.679626{col 67}{space 3} 4.223953
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 2.681213{col 26}{space 2} 2.139239{col 54}{space 4} .5612928{col 67}{space 3} 12.80776
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3a 
{txt}
{com}. 
.  cd  "$RESULTSDIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a m3a m2a using Table_SC16_country_FE.rtf, label replace  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1" "M2" "M3") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff." 1.country_FE "Country FE" ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
>  1.country_FE "Country FE" controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest  *ideol_cum *party_control *controls_total)
{res}{txt}(output written to {browse  `"Table_SC16_country_FE.rtf"'})

{com}. cd  "$DATADIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.  
.  
. *Table S-C17: Explaining partisan effects on inequality: Controlling for the relationship of countries (c) divided by the time-series length 
. 
. melogit partyeffect c.goldenshare ib0.party_control i.ineq_meas  ib0.ideol_cum  controls_total twodims c.jipf c.n_obs , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-203.47693}  
Iteration 1:{space 2}Log likelihood = {res:-201.70735}  
Iteration 2:{space 2}Log likelihood = {res:-201.70049}  
Iteration 3:{space 2}Log likelihood = {res:-201.70048}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-174.99226}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-174.99226}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-168.61862}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-167.05812}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-166.86684}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-166.86393}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-166.86385}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-166.86385}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}    33.13
{txt}Log pseudolikelihood = {res}-166.86385{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0001
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-2.045253{col 26}{space 2} 3.293674{col 37}{space 1}   -0.62{col 46}{space 3}0.535{col 54}{space 4}-8.500736{col 67}{space 3}  4.41023
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2} -1.96979{col 26}{space 2} .9577318{col 37}{space 1}   -2.06{col 46}{space 3}0.040{col 54}{space 4}-3.846909{col 67}{space 3}-.0926699
{txt}{space 12} {c |}
{space 3}ineq_meas {c |}
{space 6}Ratio  {c |}{col 14}{res}{space 2} .4209296{col 26}{space 2} .4001699{col 37}{space 1}    1.05{col 46}{space 3}0.293{col 54}{space 4}-.3633889{col 67}{space 3} 1.205248
{txt}{space 6}Share  {c |}{col 14}{res}{space 2} 2.508525{col 26}{space 2} .9241373{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .6972487{col 67}{space 3}   4.3198
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.448172{col 26}{space 2} 1.029838{col 37}{space 1}    3.35{col 46}{space 3}0.001{col 54}{space 4} 1.429726{col 67}{space 3} 5.466618
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2660014{col 26}{space 2} .1463958{col 37}{space 1}   -1.82{col 46}{space 3}0.069{col 54}{space 4}-.5529319{col 67}{space 3}  .020929
{txt}{space 5}twodims {c |}{col 14}{res}{space 2} 2.241543{col 26}{space 2} 1.889004{col 37}{space 1}    1.19{col 46}{space 3}0.235{col 54}{space 4}-1.460837{col 67}{space 3} 5.943922
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6661278{col 26}{space 2} .3412547{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-1.334975{col 67}{space 3} .0027191
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0051232{col 26}{space 2}  .002485{col 37}{space 1}    2.06{col 46}{space 3}0.039{col 54}{space 4} .0002527{col 67}{space 3} .0099937
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.104868{col 26}{space 2} 2.103216{col 37}{space 1}   -1.00{col 46}{space 3}0.317{col 54}{space 4}-6.227096{col 67}{space 3} 2.017359
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 6.168523{col 26}{space 2} 3.328668{col 54}{space 4} 2.142173{col 67}{space 3} 17.76265
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m1a
{txt}
{com}. 
. melogit partyeffect c.goldenshare toprest  ib0.ideol_cum ib0.party_control controls_total twodims  c.jipf c.n_obs , vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-202.59283}  
Iteration 1:{space 2}Log likelihood = {res:-201.57196}  
Iteration 2:{space 2}Log likelihood = {res:-201.57154}  
Iteration 3:{space 2}Log likelihood = {res:-201.57154}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-171.64425}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-171.64425}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -163.4212}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-160.22143}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-159.92307}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-159.91864}  
Iteration 5:{space 2}Log pseudolikelihood = {res: -159.9186}  
Iteration 6:{space 2}Log pseudolikelihood = {res: -159.9186}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       393
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        43

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       9.1
{col 63}{txt}max{col 67}={res}{col 69}        37

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    20.51
{txt}Log pseudolikelihood = {res}-159.9186{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0086
{txt}{ralign 78:(Std. err. adjusted for {res:43} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2} -1.29842{col 26}{space 2} 3.167234{col 37}{space 1}   -0.41{col 46}{space 3}0.682{col 54}{space 4}-7.506084{col 67}{space 3} 4.909244
{txt}{space 5}toprest {c |}{col 14}{res}{space 2} 2.358008{col 26}{space 2} 1.077385{col 37}{space 1}    2.19{col 46}{space 3}0.029{col 54}{space 4} .2463712{col 67}{space 3} 4.469644
{txt}{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 3.361466{col 26}{space 2} 1.068247{col 37}{space 1}    3.15{col 46}{space 3}0.002{col 54}{space 4}  1.26774{col 67}{space 3} 5.455191
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-1.928744{col 26}{space 2} 1.073648{col 37}{space 1}   -1.80{col 46}{space 3}0.072{col 54}{space 4}-4.033055{col 67}{space 3} .1755677
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.3452634{col 26}{space 2} .1648445{col 37}{space 1}   -2.09{col 46}{space 3}0.036{col 54}{space 4}-.6683528{col 67}{space 3} -.022174
{txt}{space 5}twodims {c |}{col 14}{res}{space 2}  1.45111{col 26}{space 2} 1.946136{col 37}{space 1}    0.75{col 46}{space 3}0.456{col 54}{space 4}-2.363245{col 67}{space 3} 5.265466
{txt}{space 8}jipf {c |}{col 14}{res}{space 2}-.6334517{col 26}{space 2} .3541844{col 37}{space 1}   -1.79{col 46}{space 3}0.074{col 54}{space 4} -1.32764{col 67}{space 3}  .060737
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0051218{col 26}{space 2} .0024808{col 37}{space 1}    2.06{col 46}{space 3}0.039{col 54}{space 4} .0002595{col 67}{space 3} .0099841
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -1.70863{col 26}{space 2} 2.194088{col 37}{space 1}   -0.78{col 46}{space 3}0.436{col 54}{space 4}-6.008964{col 67}{space 3} 2.591703
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 7.625185{col 26}{space 2} 4.452661{col 54}{space 4}  2.42772{col 67}{space 3} 23.94982
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m2a
{txt}
{com}.  
. melogit partyeffect c.goldenshare i.gini_gr  1.ideol_cum 1.party_control controls_total twodims c.jipf c.n_obs, vce(robust) ||paper_id:
{res}{txt}
Fitting fixed-effects model:

Iteration 0:{space 2}Log likelihood = {res:-87.694321}  
Iteration 1:{space 2}Log likelihood = {res:-84.293127}  
Iteration 2:{space 2}Log likelihood = {res:-84.264859}  
Iteration 3:{space 2}Log likelihood = {res:-84.264855}  

Refining starting values:

Grid node 0:{space 2}Log likelihood = {res:-79.506941}

Fitting full model:
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-79.506941}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-78.682484}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-78.612022}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-78.611518}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -78.61152}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}       188
{txt}Group variable: {res}paper_id{col 49}{txt}Number of groups{col 67}={res}{col 69}        24

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         1
{col 63}{txt}avg{col 67}={res}{col 69}       7.8
{col 63}{txt}max{col 67}={res}{col 69}        30

{txt}Integration method: {res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}    50.95
{txt}Log pseudolikelihood = {res}-78.61152{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{ralign 78:(Std. err. adjusted for {res:24} clusters in {res:paper_id})}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} partyeffect{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}goldenshare {c |}{col 14}{res}{space 2}-3.586232{col 26}{space 2} 4.057092{col 37}{space 1}   -0.88{col 46}{space 3}0.377{col 54}{space 4}-11.53799{col 67}{space 3} 4.365523
{txt}{space 12} {c |}
{space 5}gini_gr {c |}
{space 3}Post-tax  {c |}{col 14}{res}{space 2} .8096469{col 26}{space 2} 1.233451{col 37}{space 1}    0.66{col 46}{space 3}0.512{col 54}{space 4}-1.607873{col 67}{space 3} 3.227167
{txt}Pre vs. p..  {c |}{col 14}{res}{space 2} 1.503985{col 26}{space 2} .6881558{col 37}{space 1}    2.19{col 46}{space 3}0.029{col 54}{space 4} .1552242{col 67}{space 3} 2.852745
{txt}{space 12} {c |}
{space 1}1.ideol_cum {c |}{col 14}{res}{space 2} 2.928306{col 26}{space 2} 1.449199{col 37}{space 1}    2.02{col 46}{space 3}0.043{col 54}{space 4}  .087928{col 67}{space 3} 5.768684
{txt}{space 12} {c |}
party_cont~l {c |}
{space 8}yes  {c |}{col 14}{res}{space 2}-3.553983{col 26}{space 2} 1.652686{col 37}{space 1}   -2.15{col 46}{space 3}0.032{col 54}{space 4}-6.793189{col 67}{space 3}-.3147777
{txt}controls_t~l {c |}{col 14}{res}{space 2}-.2895728{col 26}{space 2} .1705326{col 37}{space 1}   -1.70{col 46}{space 3}0.089{col 54}{space 4}-.6238106{col 67}{space 3} .0446649
{txt}{space 5}twodims {c |}{col 14}{res}{space 2} 3.822697{col 26}{space 2} 2.005269{col 37}{space 1}    1.91{col 46}{space 3}0.057{col 54}{space 4}-.1075581{col 67}{space 3} 7.752951
{txt}{space 8}jipf {c |}{col 14}{res}{space 2} -.546732{col 26}{space 2} .7590455{col 37}{space 1}   -0.72{col 46}{space 3}0.471{col 54}{space 4}-2.034434{col 67}{space 3} .9409698
{txt}{space 7}n_obs {c |}{col 14}{res}{space 2} .0102972{col 26}{space 2} .0046626{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4} .0011586{col 67}{space 3} .0194358
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-3.408724{col 26}{space 2} 2.481042{col 37}{space 1}   -1.37{col 46}{space 3}0.169{col 54}{space 4}-8.271476{col 67}{space 3} 1.454029
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}paper_id    {col 14}{txt}{c |}
{space 3}var(_cons){c |}{col 14}{res}{space 2} 2.048604{col 26}{space 2} 1.825663{col 54}{space 4} .3571819{col 67}{space 3}  11.7497
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est store m3a 
{txt}
{com}. 
.  cd  "$RESULTSDIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. esttab m1a m3a m2a using Table_SC17_country_nt.rtf, label replace  compress nogaps star(* 0.10 ** 0.05 *** 0.01)  ///
> se(2) b(2) aic(2) bic(2) mtitle( "M1" "M2" "M3") nobase eqlabels(none) ///
> coeflabels( 1.party_control "Partisan eff.=control" 1.ideol_cum "Cumul. partisan eff." twodims "Countries/Time series" ///
>  2.ineq_meas "Income ratios" 3.ineq_meas "Top income shares" ///
> 1.toprest_paper "Top income (ratio & share)" jipf "JIF"   goldenshare "% of Golden age" ///
> controls_total "Number of policy channels" ///
>  n_obs "N of observations"  _cons "Constant") ///
>  order( *goldenshare *ineq_meas *gini_gr *toprest  *ideol_cum *party_control *controls_total)
{res}{txt}(output written to {browse  `"Table_SC17_country_nt.rtf"'})

{com}. cd  "$DATADIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop
{txt}
{com}.  
.  
. ****************************************
. *******OUTPUT: STUDIES AND RESULTS*******
. *****************************************
. *Table A2: Overview of included articles
. 
. *Export data for decriptive table (overview of results)
. use When_Do_Parties_Affect_Economic_Inequality_Replication.dta, clear
{txt}
{com}. 
. *Generate summary variables
. bysort paper_id ineq_meas: gen c0=_n
{txt}
{com}. bysort paper_id ineq_meas toprest: gen c1=_n
{txt}
{com}. bysort paper_id ineq_meas toprest gini_gr: gen c2=_n
{txt}
{com}. 
. su c0 c1 c2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 10}c0 {c |}{res}        393    7.381679    6.352877          1         30
{txt}{space 10}c1 {c |}{res}        393    6.916031    5.913973          1         30
{txt}{space 10}c2 {c |}{res}        393    6.173028    5.133482          1         24
{txt}
{com}. *pre-post-tax statistics
. bysort paper_id ineq_meas toprest prepost: egen peff=mean(partyeffect)
{txt}
{com}. bysort paper_id ineq_meas toprest prepost: egen goldenshare_mean=mean(goldenshare)
{txt}
{com}. bysort paper_id ineq_meas toprest prepost: egen obs_mean=mean(n_obs)
{txt}
{com}. bysort paper_id ineq_meas toprest prepost: gen pap_c=_N
{txt}
{com}. bysort paper_id ineq_meas toprest prepost: egen obs_mean_ctr=mean(controls_total)
{txt}
{com}. 
. 
. lab var goldenshare_mean "% share of Golden age (mean)"
{txt}
{com}. lab var peff "Partisan effect (mean)"
{txt}
{com}. 
. 
. keep author year obs_mean goldenshare_mean ineq_meas prepost  toprest ideol_cum party_control  paper_id peff pap_c
{txt}
{com}. 
. *Generate a single variable that stores author & publication year
. tostring year, gen(years)
{txt}years generated as {res:str4}

{com}. gen stud_lab=author+" ("+years+")"
{txt}
{com}. 
. list stud_lab in 1/3
{txt}
     {c TLC}{hline 22}{c TRC}
     {c |} {res}            stud_lab {txt}{c |}
     {c LT}{hline 22}{c RT}
  1. {c |} {res}Bradley et al (2003) {txt}{c |}
  2. {c |} {res}Bradley et al (2003) {txt}{c |}
  3. {c |} {res}Bradley et al (2003) {txt}{c |}
     {c BLC}{hline 22}{c BRC}

{com}. drop author year*
{txt}
{com}. order stud_lab obs_mean pap_c golden* ineq_meas prepost toprest ideol_cum party_control peff 
{txt}
{com}. 
. *Keep only 1 observation/paper
. duplicates drop paper_id ineq_meas toprest prepost, force

{p 0 4}{txt}Duplicates in terms of {res} paper_id ineq_meas toprest prepost{p_end}

{txt}(324 observations deleted)

{com}. 
. *Label variables for output
. lab val ideol_cum yesno_lb
{txt}
{com}. 
. lab def toprest_lb 0 "Rest" 1 "Top"
{txt}
{com}. lab val toprest toprest_lb
{txt}
{com}. 
. gen peff_r=peff*100
{txt}
{com}. drop peff
{txt}
{com}. 
. gen goldenshare_mean_r=goldenshare_mean*100
{txt}
{com}. drop goldenshare_mean
{txt}
{com}. 
. drop paper_id
{txt}
{com}. 
. order stud_lab obs_mean pap_c goldenshare_mean_r ineq_meas prepost toprest ideol_cum party_control peff 
{txt}
{com}. sort stud_lab
{txt}
{com}. 
. *Label variables for output
. lab var stud_lab "Study"
{txt}
{com}. lab var obs_mean "N (model)"
{txt}
{com}. lab var pap_c "Results / category"
{txt}
{com}. lab var goldenshare_mean_r "Golden age (%)"
{txt}
{com}. lab var peff_r "Partisan effect (%)"
{txt}
{com}. lab var ineq_meas "Measure of inequality"
{txt}
{com}. 
. cd  "$RESULTSDIR" 
{res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/results
{txt}
{com}. export excel using "Table_A2.xlsx", firstrow (varlabels) replace
{res}{txt}file {bf:Table_A2.xlsx} saved

{com}. 
. graph close
{txt}
{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/martin/Library/CloudStorage/OneDrive-Persönlich/replication/meta/upload_pop/PoP_replication.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}29 Aug 2024, 21:03:48
{txt}{.-}
{smcl}
{txt}{sf}{ul off}